How IFS is Using AI Logistics to Drive Cost Control

The world of logistics is a complex one, spanning vast global networks and adapting to a range of regulations around the world. As geopolitical volatility has reshaped logistics supply chains, more organisations are looking to implement AI in order to make them more efficient and more flexible.

Now, IFS Logistics has been launched to support enterprises as they operate complex and multi-carrier transport networks. Tackling logistics at a scale, IFS is a leading provider of Industrial AI, aiming to help businesses as they manufacture goods, maintain complex assets and manage service-focused operations. Through this, it is gaining advancements in productivity, efficiency and sustainability.

Through the use of AI, machine learning, real-time data and analytics, IFS helps its customers to make more informed strategic decisions. Now, it has launched IFS.ai Logistics, an AI-powered logistics intelligence platform which is purpose-built for enterprises which operate complex, multi-carrier and multi-region transport networks.

This could help Industrial AI ease into the physical movement of materials and goods, ensuring enterprises around the world can keep working efficiently. Already, IFS manages US$2.4tn in critical assets for its customers.

Connecting operations to financial outcomes

With new capabilities unlocked by IFS.ai Logistics, there is a new intelligence layer which will connect operational decisions to financial outcomes across the entire supply chain. IFS acquired 7bridges in 2025 and is now using its technology as a starting point for IFS.ai Logistics.

The logistics platform goes beyond this existing technology, working to deliver a single closed operational loop which spans transport planning, automated execution, freight audit, cost governance and continuous network optimisation.

It is operating within IFS Cloud, in tandem with Enterprise Asset Management, Field Service Management, Enterprise Resource Planning and Supply Chain Management. It is designed to work with third-party platforms, which limits adoption friction for companies which manage complex multi-system environments.

Philip Ashton, President at IFS.ai Logistics, explains the impact on costs: “Logistics is one of the largest, most frequently disrupted and least-governed cost categories in global industry, and the consequences show up directly in EBITDA,” says Philip.

"Over the last five years we have seen that when AI is applied at scale, directly inside specific industry applications, like enterprise logistics operations, customers can capture value within weeks, they begin to protect margins, improve service reliability and increase operational agility.

"With IFS.ai Logistics, this is exactly what we are delivering: an AI-driven platform that closes the loop between every operational logistics decision and its financial consequence. This is Industrial AI applied where it matters most.”

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Addressing critical market gaps

A strong logistics network is vital for resilient business operations. According to industry data, businesses today spend 5% to 10% of revenue on transportation, however logistics is one of the most difficult costs to govern.

It is often a global network, which puts it at risk of fragmented data across carriers, regions, legacy systems and outdated spreadsheets. As a result, logistics teams are having to react to events, as they cannot see a full view of their data, meaning they cannot trust or compare it.

Manufacturing and logistics providers which operate on a large scale can experience costly impacts from even a minor error. For these businesses, even a 1% inefficiency in freight spend can represent hundreds of millions of dollars in avoidable annual cost.

This could mean the industry requires a total structural transformation, rather than automation simply applied to the network in incremental steps. The launch of IFS.ai Logistics represents the company's expansion across the physical supply chain, creating a more connected flow of materials from warehouse operations to final delivery.

Keith Kirkpatrick, VP and Research Director at The Futurum Group, comments on the research surrounding these deployments:

"Futurum's research shows that nearly half of enterprise decision-makers are planning agentic AI deployments in supply chain management, and the supply chain software segment is accelerating toward double-digit annual growth through 2031," explains Keith.

"Yet the current vendor landscape remains dominated by legacy planning and execution tools that were never designed for AI-native intelligence. IFS.ai Logistics addresses a genuine market gap, bringing closed-loop AI across transport planning, execution, audit and optimisation into a single platform.

"For industrial enterprises spending 5% to 10% of revenue on freight and transportation, the ability to connect every logistics decision to its financial consequence is transformational.”

Optimising transport planning features

IFS.ai Logistics is aiming to address four main concerns across key capability areas. AI-driven transport planning and carrier selection will be replacing manual decision-making with intelligence-led optimisation.

This will occur across a range of modes, legs and trade lanes for more efficient decisions. Zero-touch automated execution will significantly reduce booking errors and operational overhead, due to the inclusion of real-time shipment visibility and exception handling.

Invoices at line-item level will be validated by finance-grade freight audit engines. This will rely on automated GL coding, which will demonstrate billing discrepancies and manage dispute workflows to recover leakage.

Continuous what-if scenario modelling takes place due to a thorough network intelligence and simulation layer, utilising carrier strategy, cost forecasting, emissions planning and procurement consolidation.

This will standardise and harmonise fragmented transport data for a single trusted intelligence layer, which uses one source of truth to enable stronger, more accurate decision-making across the logistics network.