AI Display Scoring for the Beer & Beverage Industry

23/10/2026

Lessons from a Misaligned Rollout

I once worked on a display scoring project for a major beverage brand in Vietnam. The initial plan looked promising: use AI Visibility to fully automate retail point checks. But after three months, we realized we had made a mistake. We focused too much on algorithmic accuracy and overlooked a simple reality: field data collectors did not want to take 50 photos per visit. They just wanted to snap three quick shots and leave. As a result, we collected very little data, and no matter how good the AI system was, it had nothing to process. The lesson learned: AI does not replace processes; it only amplifies them. If the underlying process is outdated, AI will only make that obsolescence more apparent. In this article, I will share how to build a practical AI display scoring process, focusing on VTraks and the critical decisions you need to make from the start.

Step 1: Define the Objectives of Display Scoring

Before touching any technology, sit down with your sales and marketing teams to answer one question: what do you want to know from these photos? Many businesses think they want to know the "planogram compliance rate." In reality, they need to know "why the product is missing from the shelf" or "how much lower the competitor's price is." Different objectives lead to different photography and scoring methods. If the goal is to detect counterfeits, you need close-up shots of QR codes. If the goal is to check product placement, you need full-shelf panoramic shots. Write down three specific, measurable objectives. For example: "Detect 90% of cases where our product is obscured by competitors." Avoid vague goals like "increase sales." That will not help design the data collection process. This is the most important step, yet the one most often overlooked.

Step 2: Design the Data Collection Process at the Point of Sale

Remember the lesson above? Data collectors will not do anything overly complex. Therefore, the photography process must be extremely simple. I recommend standardizing a set of three photos: one wide shot of the store, one close-up of the main shelf, and one close-up of your product. Each photo needs clear criteria: shooting distance, angle, and lighting. Create a short visual guide, print it out, and distribute it to the team. Do not write lengthy documents. A good sample image is worth ten pages of text. Then, pilot this process in 5–10 stores before a full rollout. Observe how long it takes the collectors to finish the photo set. If it takes more than five minutes, the process is not good enough. The goal is to reduce the time to under three minutes. This requires patience in the early stages, but it will determine the success or failure of the entire project.

Step 3: Choose the Right AI Visibility Technology

The market offers many AI Vision solutions, but not all are suitable for the beer and beverage industry. You need a system capable of accurately identifying each SKU, especially when products have similar packaging. AIVISION's VTraks is a worthy consideration. According to vtraks.com, the system achieves 99.97% accuracy and processes images in under one second. These figures are crucial because they ensure data is processed quickly, allowing sales teams to receive timely feedback. However, do not look at accuracy alone. Ask about the ability to detect fake or duplicate images. This is a common issue during large-scale rollouts. Some staff may retake old photos to cheat. The AI system needs to be able to detect this. Additionally, consider integration capabilities with other systems you are using, such as CRM or ERP. An isolated AI system will not create real value. It needs to be part of the enterprise's overall information system.

Step 4: Establish Scoring Criteria and Alerts

Once you have the data, the next step is to turn it into useful insights. You need to clearly define what constitutes "compliance" and what does not. For example, the product must occupy at least 50% of the designated shelf space. Or, the product must be at consumer eye level. Work with the marketing team to build these criteria. Do not let the sales team decide on their own, as they may be biased. Then, set up alert levels. Which alerts require immediate action? Which can wait until the end of the week? For example, if the product is completely removed from the shelf, that is an emergency alert. If it is partially obscured, that is a standard alert. Display these alerts on an intuitive dashboard. Do not just send emails. Employees need to see the issue as soon as they open their phone. A good dashboard will help them prioritize their work reasonably.

Quick Answers

How long does implementation take?

Typically 4 to 8 weeks, depending on scale and the complexity of the scoring criteria. The initial phase takes longer to refine the data collection process.

What is the initial investment cost?

Costs depend on the number of stores and the features you use. Request a specific quote from the provider. Do not compare costs per store mechanically, as each project has different requirements.

Do I need to change my current systems?

Not necessarily. Many AI systems can integrate with existing mobile or web applications. However, you need to ensure that data from the AI flows smoothly into your reporting systems.

Step 5: Measure Performance and Improve Continuously

Implementation is not the end. You need to continuously measure the system's performance. Track metrics such as: the percentage of stores checked, average processing time per image, the rate of resolved alerts, and most importantly, the impact on sales. Do not just look at AI accuracy. Look at human behavior. Has the sales team changed how they work? Are they responding to alerts faster? If not, go back and review the process. The scoring criteria may be too strict, or the alerts may not be clear enough. Create a short feedback loop where the sales team can report issues they encounter. This continuous improvement will help the system become increasingly aligned with point-of-sale realities. This is a process with no end point, but it is the deciding factor in turning AI from a tool into a sustainable competitive advantage.

AIVISION builds computer vision, Agentic AI and custom AI software for manufacturers and retailers. Browse our services, try the AI assistant, or send us your problem.