• Written by Azhar Aliyar, Director

A dusty dashboard, once the centrepiece of the IT department's efforts to bring data to the business, now sits idle, a relic of a bygone era when every question seemed to demand a new report, a new system, or a new integration, all of which contribute to the ever-growing IT spend in the automotive industry.

Where IT money actually goes in the automotive industry

IT spending in the automotive industry grows by accretion, with each new question or requirement adding another layer of complexity, and therefore cost, to the existing infrastructure. Much of this spending is focused on getting answers out of systems rather than running them, with significant resources devoted to reporting, data extraction, and integration. 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 the automotive industry, this figure is likely lower, with 12.4% of US manufacturers, the Census sector that includes vehicle and parts makers, reported using AI in the previous two weeks.

This highlights the opportunity for AI to make a significant impact in the industry, particularly in areas such as IT spending, where the use of AI can help to reduce costs and improve efficiency. By understanding where IT money actually goes, businesses can begin to identify areas where AI can be used to reduce spending and improve productivity.

The report and dashboard backlog

The report and dashboard backlog is a major contributor to IT spending in the automotive industry, with each new question or requirement adding another report, another dashboard, or another system to the existing infrastructure. This approach is not only costly but also inefficient, as it leads to a proliferation of systems and reports, each of which requires maintenance, support, and updates. By using AI to answer questions and provide insights, businesses can reduce the need for new reports and dashboards, and therefore reduce the associated costs.

For example, a category manager comparing two suppliers might previously have required a new report to be generated, but with AI, this information can be provided in real-time, without the need for a new report or dashboard. This approach not only reduces costs but also improves the speed and accuracy of decision-making.

Extending MES, PLM, supplier portals and the warranty system

Rather than replacing existing systems such as MES, PLM, supplier portals, and the warranty system, AI can be used to extend their functionality and provide new insights and capabilities. By asking better questions of the systems you already own, businesses can defer replacement programmes and reduce the associated costs. This approach also allows businesses to get more value from their existing investments, rather than simply replacing them with new systems.

For instance, a shift supervisor checking the maintenance log might use AI to identify potential issues before they become major problems, reducing downtime and improving overall efficiency. This approach not only reduces costs but also improves the effectiveness of the existing systems.

Integration spend

Integration spend is another major contributor to IT spending in the automotive industry, with businesses often building and maintaining point-to-point links between different systems. 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 also allows businesses to provide more flexible and dynamic integration, rather than being limited by fixed point-to-point links.

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, and only 6.8% changed how they collect or manage data. This highlights the opportunity for businesses to rethink their approach to integration and use AI to reduce costs and improve efficiency.

Where AI adds cost rather than removing it

While AI can be used to reduce IT spending in the automotive industry, 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 solutions can be significant, and businesses must budget for these costs honestly. This includes investing in data quality, data governance, and data security, as well as ensuring that the necessary skills and expertise are available to implement and maintain AI solutions.

Businesses must also be aware of the potential risks and challenges associated with AI, such as bias in decision-making, and ensure that they have the necessary controls and safeguards in place to mitigate these risks. By being aware of these potential costs and challenges, businesses can ensure that they get the most value from their AI investments.

How to tell whether IT spending actually fell

To determine whether IT spending has actually fallen, businesses must measure the reduction in spending against the requests and licences that stopped, rather than against a projection. This requires a detailed understanding of the existing IT infrastructure and the costs associated with it, as well as a clear understanding of the benefits and savings provided by AI. By using expert advice and guidance, businesses can ensure that they are measuring the impact of AI on IT spending accurately and effectively.

For example, a business might measure the reduction in spending on reports and dashboards, or the reduction in the number of licences required for existing systems. By using a data-driven approach, businesses can ensure that they are getting the most value from their AI investments and reducing IT spending effectively. As discussed in our previous articles, this approach requires strong leadership and a clear understanding of the benefits and challenges associated with AI.

What this does not do

This approach to reducing IT spending in the automotive industry using AI does not replace the need for a clear understanding of the business and its requirements. It also does not eliminate the need for skilled and experienced IT professionals, who are essential for implementing and maintaining AI solutions. As outlined in our blog series, AI is a tool that can be used to support and enhance the existing IT infrastructure, but it is not a replacement for it.

Furthermore, this approach is not a one-size-fits-all solution, and businesses must carefully consider their specific needs and requirements before implementing AI solutions. By using a tailored approach, businesses can ensure that they get the most value from their AI investments and reduce IT spending effectively. 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, highlighting the varying levels of AI adoption across different industries and functions.