• Written by Muhammed Abdulla NC, CEO

The daily report from the enterprise resource planning system lies unopened on the desk of the category manager, a testament to the fact that AI has not yet transformed every aspect of FMCG operations. However, in the office, AI has genuinely changed day-to-day work, particularly in functions whose output is text, such as sales and marketing. According to the US Census Bureau's Business Trends and Outlook Survey, 12.4% of US manufacturers, the Census sector that includes FMCG producers, reported using AI in the previous two weeks, with 14.3% of US businesses using AI in sales and marketing in the previous six months.

Where AI has actually landed in FMCG so far

AI has largely landed in the office functions of FMCG businesses, such as sales, marketing, and category management. These functions are more likely to have outputs that are text-based, making them more amenable to AI-powered tools. For example, a category manager can use AI to analyze sales data and generate reports on product performance. This has freed up time for category managers to focus on higher-value tasks, such as negotiating with suppliers and developing marketing campaigns.

In contrast, AI has not yet had a significant impact on the operational aspects of FMCG, such as manufacturing and logistics. This is because these functions are more focused on physical work, such as managing inventory and coordinating transportation. However, as AI technology continues to evolve, we can expect to see more applications in these areas.

Promotional distortion in history

Promotional distortion, or the impact of promotions on demand, has long been a challenge for FMCG businesses. Historically, dealing with promotional distortion has involved waiting for reports from the ERP system, the distributor management system, trade promotion tools, and demand planning. However, with AI-powered tools, category managers can now ask the data a direct question and get an answer in real-time. For example, a category manager can ask, "What was the impact of the recent price promotion on sales of our flagship product?" and get an immediate answer.

This has significantly reduced the time and effort required to analyze promotional distortion, allowing category managers to make more informed decisions about pricing and promotions. It has also enabled them to respond more quickly to changes in the market, such as a competitor's price promotion.

Shelf life write-offs

Shelf life write-offs, or the disposal of products that are near or past their expiration date, are a significant cost for FMCG businesses. Currently, decisions about shelf life write-offs often require a person to stitch together data from two systems, such as the ERP system and the inventory management system. However, with AI-powered tools, this process can be automated, and answers can be provided in real-time.

For example, a supply chain manager can ask, "What products are near their expiration date and need to be written off?" and get an immediate answer. This has reduced the time and effort required to manage shelf life write-offs, and has also helped to minimize waste and reduce costs.

Distributor visibility

Distributor visibility, or the ability to see what products are in stock at distributor locations, is a critical aspect of FMCG operations. However, achieving distributor visibility can be harder than it looks, particularly if the distributor is using a different system or has limited visibility into their own inventory. Before AI can help with distributor visibility, several things need to be in place, such as a common data standard and a system for sharing data between the FMCG business and the distributor.

Once these foundations are in place, AI-powered tools can help to provide real-time visibility into distributor inventory, enabling FMCG businesses to make more informed decisions about production and shipping. For example, a logistics manager can ask, "What products are in stock at our largest distributor?" and get an immediate answer.

Why most adopters have not changed anything underneath

Despite the potential of AI to transform FMCG operations, most adopters have not changed anything underneath, according to the US Census Bureau's Business Trends and Outlook Survey, which found that 64.3% of US businesses made no change of any kind to adopt AI. This is because AI is often seen as a tool that can be layered on top of existing systems and processes, rather than a catalyst for fundamental change.

However, this approach can limit the benefits that can be achieved with AI. For example, if an FMCG business is using AI to analyze sales data, but has not changed its underlying sales processes, it may not be able to take full advantage of the insights provided by the AI tool. To get the most out of AI, FMCG businesses need to be willing to rethink their underlying systems and processes.

What a sensible first quarter looks like for a FMCG business

A sensible first quarter for a FMCG business looking to adopt AI might involve focusing on a single question, such as "What is the impact of price promotions on sales of our flagship product?" and a single system, such as the ERP system. It might also involve working with a single team, such as the category management team, to develop and implement an AI-powered tool.

This approach can help to build momentum and demonstrate the value of AI, while also minimizing the risk of disruption to existing operations. For more information on how to get started with AI, contact us or visit our sales teams page.

What this does not do

AI is not a panacea for all the challenges facing FMCG businesses. It is not a replacement for human judgment and expertise, and it is not a guarantee of success. In some cases, AI may not be the right choice, such as when the data is incomplete or inaccurate, or when the problem is too complex or nuanced.

For example, AI may not be able to provide a complete solution for managing shelf life write-offs, as this often requires a deep understanding of the underlying business processes and systems. In such cases, a more traditional approach may be needed, such as working with a consultant or implementing a new system. For more information on the limitations of AI in FMCG, visit our blog.