• Written by Azhar Aliyar, Director

When a manufacturing business needs to answer a new question about its operations, the usual response is to commission a new report, which in turn leads to a new integration project, and often a new system purchase, resulting in ever-rising IT costs. This cycle of accretion can be broken by using AI to ask better questions of the existing systems, such as ERP, MES, the plant historian, and the maintenance system, thereby reducing the need for new reports, integrations, and system purchases.

Where IT money actually goes in a manufacturing business

Most IT spending in manufacturing is not on running the systems, but on getting answers out of them. This is because each new question seems to require a new report, a new dashboard, or even a new system, leading to a never-ending cycle of spending. According to the US Census Bureau's Business Trends and Outlook Survey, 22.4% of US businesses reported using AI in the two weeks to July 2026, but in manufacturing, this figure is lower, at 12.4%, indicating a significant opportunity for improvement. The money spent on IT is largely consumed by the salaries of the people who build and maintain these reports, integrations, and systems.

A shift supervisor, for example, may need to check the maintenance log, the production schedule, and the inventory levels to answer a simple question about whether a particular machine is available for production. If the systems are not integrated, or if the reports are not available, the shift supervisor may need to request a new report, which can take weeks or even months to deliver, and may require significant IT resources.

The report and dashboard backlog

The report and dashboard backlog is a major contributor to IT costs in manufacturing. Each new question seems to require a new report, which can take weeks or even months to deliver, and may require significant IT resources. This backlog can be replaced by using AI to answer questions in real-time, using the data already available in the existing systems. For instance, a category manager can use AI to compare two suppliers, without needing to wait for a new report to be generated.

By using AI to answer questions in real-time, the report and dashboard backlog can be significantly reduced, freeing up IT resources to focus on more strategic initiatives. This approach can also help to reduce the number of requests for new reports, and the associated costs, as users can get the answers they need quickly and easily, without needing to rely on IT.

Extending ERP, MES, the plant historian and the maintenance system instead of replacing them

Rather than replacing existing systems, such as ERP, MES, the plant historian, and the maintenance system, AI can be used to extend their capabilities, and ask better questions of the data they contain. This approach can help to defer a replacement programme, and reduce the associated costs. According to the US Census Bureau's Business Trends and Outlook Survey, 64.3% of US businesses made no change of any kind to adopt AI, indicating that many businesses are already using AI to extend the capabilities of their existing systems.

For example, a manufacturing business can use AI to analyze the data in its ERP system, to identify trends and patterns that may not be immediately apparent. This can help to improve decision-making, and reduce the need for new reports, or system purchases. By extending the capabilities of existing systems, businesses can get more value from their IT investments, and reduce the costs associated with replacing them.

Integration spend

Integration spend is another major contributor to IT costs in manufacturing. Each new system purchase, or integration project, can require significant IT resources, and may take weeks or even months to deliver. By using AI to join data at the point of the question, rather than building and maintaining a point-to-point link for each one, businesses can reduce the costs associated with integration. This approach can also help to improve data quality, and reduce the risks associated with data integration.

For instance, a manufacturing business can use AI to integrate data from its ERP system, with data from its maintenance system, to get a complete view of its operations. This can help to improve decision-making, and reduce the need for new reports, or system purchases. By using AI to integrate data, businesses can get more value from their IT investments, and reduce the costs associated with integration.

Where AI adds cost rather than removing it

While AI can help to reduce IT costs in manufacturing, it is not a panacea, and there are areas where it can add cost, rather than removing it. For example, the data work and governance required to implement AI can be significant, and may require additional IT resources. According to the US Census Bureau's Business Trends and Outlook Survey, 6.8% of US businesses changed how they collect or manage data to adopt AI, indicating that many businesses are already investing in data governance and management.

To budget for AI honestly, businesses need to consider the costs associated with data governance and management, as well as the costs associated with implementing and maintaining AI systems. This may require additional IT resources, and may take weeks or even months to deliver. However, by being honest about the costs, and benefits, of AI, businesses can make informed decisions about how to use it to reduce IT costs, and improve their operations. For more information on how to implement AI in manufacturing, please visit our blog.

How to tell whether IT spending actually fell

To determine whether IT spending has actually fallen, businesses need to measure the reduction in requests for new reports, and licences, rather than relying on projections. This can be done by tracking the number of requests for new reports, and licences, and comparing it to the number of requests received before implementing AI. By using this approach, businesses can get a clear view of the impact of AI on their IT spending, and make informed decisions about how to use it to reduce costs.

For example, a manufacturing business can track the number of requests for new reports, and licences, and compare it to the number of requests received before implementing AI. If the number of requests has decreased, it may indicate that AI is having a positive impact on IT spending. To get more information on how to measure the impact of AI on IT spending, please contact us.

What this does not do

This approach to reducing IT costs in manufacturing does not replace the need for strategic leadership, and vision. According to our research, executive and strategic leadership are critical to the success of any IT initiative, including the use of AI to reduce IT costs. Businesses still need to have a clear understanding of their operations, and the role that IT plays in supporting them, in order to make informed decisions about how to use AI to reduce costs.

Additionally, this approach may not be suitable for all businesses, particularly those with very complex, or customized systems. In these cases, the costs associated with implementing AI may outweigh the benefits, and other approaches may be more suitable. By understanding the limitations of this approach, businesses can make informed decisions about how to use AI to reduce IT costs, and improve their operations. 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, which highlights the potential for AI to be used in other areas of the business, such as sales and marketing, to drive growth and improvement. US manufacturers used AI in production of goods at 2.2%, against 1.7% across all businesses, indicating a significant opportunity for improvement in the manufacturing sector. Among US businesses with no plans to adopt AI, 61.6% said it was not applicable to them and only 6.9% said it was too expensive, according to the US Census Bureau's Business Trends and Outlook Survey, which suggests that many businesses are not yet aware of the potential benefits of AI, and may need more information and guidance to get started. These figures are specific to the US, and may not reflect the situation in other countries. However, they do provide a useful insight into the current state of AI adoption in US businesses, and the potential for AI to be used to reduce IT costs and improve operations.