• Written by Muhammed Abdulla NC, CEO

A category manager stares at a screen displaying the latest sales data, trying to make sense of the numbers in the context of the current promotional campaign, and wondering why the company's new enterprise AI system is not being used to its full potential, despite its ability to answer questions about the organisation's own data in real time.

Challenges in FMCG

The fast-moving consumer goods industry poses unique challenges to the adoption of new systems, including enterprise AI. Promotional distortion in historical data makes it difficult to identify true demand patterns, while short shelf life and write-offs add complexity to inventory management. Distributor visibility is also a challenge, as companies often rely on third-party distributors to get their products to market.

These challenges can lead to a situation where capable systems are underutilized, as users struggle to integrate them into their existing workflows. For example, a shift supervisor may continue to rely on manual checks of inventory levels, rather than using the AI system to provide real-time updates. This can result in missed opportunities to optimize supply chain operations and improve customer satisfaction.

Attaching new capability to existing routines

To overcome these challenges, it is essential to attach new capability to routines that already exist. This means identifying areas where the AI system can augment existing processes, rather than trying to replace them entirely. For instance, the category manager could use the AI system to analyze sales data and identify opportunities to optimize promotional campaigns, while still relying on existing systems for inventory management.

By building on existing routines, users can develop trust in the new system and begin to see its value in their daily work. This approach also helps to minimize disruption to existing workflows, making it more likely that the new system will be adopted and used effectively. As users become more comfortable with the system, they can begin to explore its full capabilities and identify new ways to use it to drive business value.

Who needs to ask, not just who needs the answer

Most people get wrong the idea that only certain users need access to the AI system, such as data analysts or category managers. However, this is wrong, as anyone who needs to make decisions based on data should be able to ask questions and get answers. This includes supply chain managers, sales teams, and even customer service representatives.

By providing access to the AI system to a broader range of users, companies can unlock its full potential and drive business value. For example, a sales team member could use the system to answer customer questions about product availability, while a supply chain manager could use it to identify potential bottlenecks in the supply chain. To learn more about how AsscherAi can support your organisation, visit our website.

The first wrong answer and rebuilding trust

The first time the AI system provides a wrong answer, it can be a significant setback. Users may lose trust in the system and revert to their old ways of working. However, this is not the end of the story. By acknowledging the mistake and taking steps to correct it, companies can rebuild trust in the system and encourage users to continue exploring its capabilities.

This may involve retraining the AI model, updating the data it is using, or providing additional training to users. Whatever the approach, it is essential to be transparent and communicative throughout the process, keeping users informed of progress and involving them in the solution. By doing so, companies can turn a potential setback into an opportunity to improve the system and drive even greater business value.

What this does not do

While the AI system can provide significant benefits to FMCG companies, it is not a silver bullet. It will not replace the need for human judgment and decision-making, nor will it provide answers to questions that are not based on data. Additionally, it is not a substitute for existing systems, but rather a complementary tool that can help users make better decisions.

Before implementing an AI system, companies should carefully consider their goals and objectives, and ensure that the system is aligned with their overall business strategy. They should also be prepared to invest time and resources in training and supporting users, as well as in maintaining and updating the system over time. For more information on how to get started with AsscherAi, contact our team.