• Written by Azhar Aliyar, Director

A category manager for a large textiles and apparel business sits at her desk, staring at a stack of reports and dashboards, each one answering a slightly different question about the same set of data, and wonders why the IT budget seems to grow every year without ever actually reducing the number of questions she has to ask. The answer lies in where the money is actually spent: not on running the systems, but on getting answers out of them.

Where IT money actually goes in a textiles and apparel business

The IT budget for a textiles and apparel business is often dominated by the cost of extracting insights from the various systems that run the business, from Enterprise Resource Planning (ERP) to Product Lifecycle Management (PLM) and production planning tools. This is because each of these systems holds a piece of the puzzle, but getting them to talk to each other in a way that answers the questions the business needs to ask is a complex and expensive task. As a result, a significant portion of the IT budget goes towards building custom reports and integrations, rather than actually running the systems themselves.

This is reflected in the fact that, according to the US Census Bureau's Business Trends and Outlook Survey, 14.3% of US businesses used AI in sales and marketing in the previous six months, against 1.7% in production of goods and 0.9% in distribution. This suggests that businesses are focusing on using AI to extract insights from their data, rather than to automate or optimize their core operations.

The report and dashboard backlog

The report and dashboard backlog is a major contributor to the growth of IT spending in textiles and apparel. Every time a new question needs to be answered, a new report or dashboard needs to be built, which requires significant time and resources. This can lead to a situation where the IT department is constantly playing catch-up, trying to build new reports and dashboards to keep up with the demands of the business. By using AI to answer questions directly from the data, rather than relying on pre-built reports and dashboards, businesses can break this cycle and reduce their IT spending.

For example, a shift supervisor might use AI to ask a question about production schedules, rather than relying on a pre-built report. This allows the supervisor to get the answer they need quickly and easily, without having to wait for the IT department to build a new report.

Extending ERP, PLM, production planning and merchandising tools instead of replacing them

Rather than replacing existing ERP, PLM, production planning and merchandising tools, businesses can use AI to extend their functionality and get more out of the systems they already own. This can be done by using AI to ask better questions of the systems, and to extract insights that would otherwise be difficult or impossible to get. By doing so, businesses can defer a replacement programme and reduce their IT spending.

This approach can also help businesses to get more value out of their existing systems, rather than simply throwing them away and starting from scratch. As the executive and strategic leadership of the business will know, this can be a major cost savings, and can help to reduce the risk of a major IT project.

Integration spend

Integration spend is another major contributor to the growth of IT spending in textiles and apparel. Every time a new system needs to be integrated with an existing one, significant time and resources are required. 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 their integration spend and get more out of their existing systems.

According to the US Census Bureau's Business Trends and Outlook Survey, 12.4% of US manufacturers, the Census sector that includes textile and apparel makers, reported using AI in the previous two weeks. This suggests that businesses in this sector are starting to see the value of using AI to reduce their IT spending and get more out of their existing systems.

Where AI adds cost rather than removing it

While AI can be a powerful tool for reducing IT spending in textiles and apparel, it is not a panacea. There are certain areas where AI can actually add cost, rather than removing it. For example, the data work and governance required to implement an AI system can be significant, and cannot be skipped. Businesses need to be honest about these costs, and budget for them accordingly.

As the blog on how AI is transforming textiles notes, AI is not a replacement for human judgment and expertise. Businesses need to make sure that they have the right people and processes in place to get the most out of their AI system, and to ensure that it is used in a way that is transparent and accountable.

How to tell whether IT spending actually fell

Measuring the success of an AI project in reducing IT spending can be difficult, but there are certain metrics that businesses can use to determine whether their spending has actually fallen. For example, they can look at the number of reports and dashboards that are no longer needed, or the number of integrations that are no longer required. They can also look at the number of licences that are no longer needed, or the number of IT staff that are no longer required to build and maintain custom reports and integrations.

By using these metrics, businesses can get a clear picture of whether their AI project has actually reduced their IT spending, and can make adjustments as needed. As the contact us page notes, this can be a complex and challenging process, but it is one that is essential for getting the most out of an AI system.

What this does not do

This approach to reducing IT spending in textiles and apparel using AI is not a silver bullet, and there are certain situations where it may not be the right choice. For example, businesses that are already heavily invested in a particular system or technology may find that it is more cost-effective to stick with what they have, rather than trying to implement an AI system. Similarly, businesses that do not have the right data or infrastructure in place may find that an AI system is not feasible.

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, and only 6.9% said it was too expensive. This suggests that businesses need to carefully consider their own needs and circumstances before deciding whether to implement an AI system, and should not simply assume that it will be the right choice for them.