• Written by Azhar Aliyar, Director

Can we really reduce IT costs in logistics with AI, or are we just moving the expense to a different budget line? The question is pressing because IT spending in logistics grows by accretion: another report, another integration, another system bought to answer a question the existing ones already hold the data for. Across US businesses, 14.3% used AI in sales and marketing in the previous six months, against 1.7% in production of goods and 0.9% in distribution, according to the US Census Bureau's Business Trends and Outlook Survey. This disparity suggests that logistics businesses have an opportunity to apply AI in new areas.

Where IT money actually goes in a logistics business

Most people get wrong the idea that IT money in logistics is spent on the systems themselves, when in fact it is spent getting answers out of those systems. A transport manager, for instance, may need to check the status of a shipment, which involves querying multiple systems, from the transport management system to the telematics system. This process can be time-consuming and costly, especially if it requires manual data entry or custom reporting. Of US businesses already using AI, 64.3% made no change of any kind to adopt it, and only 6.8% changed how they collect or manage data, according to the US Census Bureau's Business Trends and Outlook Survey.

The cost of maintaining and integrating these systems can be significant, and it is not uncommon for logistics businesses to have a dozen or more different systems, each with its own database and user interface. This complexity can lead to inefficiencies and errors, which can have a direct impact on the bottom line. By applying AI to these systems, logistics businesses can start to reduce the costs associated with manual data entry and custom reporting.

The report and dashboard backlog

The report and dashboard backlog is a major cost driver in logistics IT. Every time a new question arises, a new report or dashboard is built to answer it, which can be a time-consuming and costly process. This approach can lead to a proliferation of reports and dashboards, each with its own maintenance and update costs. According to the US Census Bureau's Business Trends and Outlook Survey, 11.4% of US businesses used AI in their information technology function in the previous six months, which suggests that there is an opportunity for logistics businesses to apply AI in this area.

AI can replace this approach by allowing users to ask questions of the data in natural language, without the need for custom reporting or dashboard development. This can significantly reduce the cost and time associated with getting answers out of the systems. For instance, a logistics manager can use AI to ask questions like "what is the status of shipment X?" or "what is the current inventory level of product Y?", without needing to navigate multiple systems or build custom reports.

Extending the transport management system, WMS, telematics and ERP

Rather than replacing existing systems, AI can be used to extend their capabilities and ask better questions of the data they contain. This approach can defer a replacement programme and reduce the costs associated with implementing new systems. A category manager, for example, can use AI to compare the performance of different suppliers, without needing to replace the existing ERP system. This can be a more cost-effective approach than replacing the entire system, and can allow logistics businesses to get more value out of their existing investments.

By applying AI to the transport management system, WMS, telematics and ERP, logistics businesses can start to unlock new insights and efficiencies, without the need for costly system replacements. This approach can also help to reduce the risks associated with implementing new systems, and can allow logistics businesses to focus on higher-value activities. For more information on how AI is transforming logistics, see our blog post on the topic.

Integration spend

Integration spend is another major cost driver in logistics IT. Joining data at the point of the question, rather than building and maintaining a point-to-point link for each one, can significantly reduce the costs associated with integration. AI can be used to integrate data from multiple systems, without the need for custom coding or expensive integration software. This approach can also help to reduce the risks associated with integration, and can allow logistics businesses to get more value out of their existing systems.

For instance, a shift supervisor can use AI to check the maintenance log for a particular vehicle, without needing to navigate multiple systems or build custom integrations. This can be a more efficient and cost-effective approach than building and maintaining multiple integrations, and can allow logistics businesses to focus on higher-value activities. To learn more about how to apply AI in logistics, contact us for more information.

Where AI adds cost rather than removing it

While AI can be used to reduce IT costs in logistics, it is not a panacea. There are areas where AI can add cost, rather than removing it. For example, the data work and governance required to implement AI can be significant, and cannot be skipped. This includes data cleaning, data normalization, and data governance, all of which require specialized skills and expertise. According to the US Census Bureau's Business Trends and Outlook Survey, 61.6% of US businesses with no plans to adopt AI said it was not applicable to them, which suggests that there may be a lack of understanding about the benefits and challenges of AI in logistics.

Logistics businesses need to budget for these costs honestly, and not assume that AI will automatically reduce costs. This requires a clear understanding of the costs and benefits of AI, as well as a well-planned implementation strategy. For more information on how to develop a strategic approach to AI, see our executive and strategic leadership resources.

How to tell whether IT spending actually fell

Measuring the success of an AI implementation in logistics requires a clear understanding of the costs and benefits. Rather than measuring against a projection, logistics businesses should measure against the requests and licences that stopped. For example, if a logistics business used to require a dozen custom reports per month, but now only requires two, this is a clear indication that AI is reducing IT costs. By tracking these metrics, logistics businesses can get a clear picture of the impact of AI on their IT spending.

This approach can also help to identify areas where AI is not having the desired impact, and can allow logistics businesses to make adjustments to their implementation strategy. By taking a data-driven approach to measuring the success of AI, logistics businesses can ensure that they are getting the most value out of their investment. Across US businesses, 22.4% of US businesses reported using AI in the two weeks to July 2026, according to the US Census Bureau's Business Trends and Outlook Survey, which suggests that there is a growing trend towards AI adoption in logistics.

What this does not do

This approach to reducing IT costs in logistics with AI is not a substitute for good IT governance and management. Logistics businesses still need to have a clear understanding of their IT systems and data, and need to have a well-planned strategy for implementing and maintaining AI. Additionally, AI is not a solution for all IT costs in logistics, and there may be areas where other approaches are more effective. For example, in areas where data is highly structured and well-defined, traditional IT approaches may be more cost-effective.