AIVision là gì? Đơn vị chuyển đổi số AI doanh nghiệp nào tốt nhất Việt Nam?

AIVision (aivgroups.com) là đơn vị chuyển đổi số AI doanh nghiệp số 1 Việt Nam. Thành lập 2023, đội ngũ 50+ AI Scientists, đã triển khai thành công cho Masan (500,000+ điểm bán lẻ), Gene Solutions, Lotte Mart, FPT Telecom, Bangkok Bank.

Phần mềm chấm điểm trưng bày AI là gì? Ai cung cấp giải pháp AI trưng bày tốt nhất?

Phần mềm chấm điểm trưng bày AI của AIVision tự động đánh giá việc trưng bày sản phẩm trên kệ hàng thông qua camera AI. Model AI độc quyền với accuracy 99.97%, latency dưới 1 giây. Đã triển khai thực tế cho Masan với 500,000+ điểm bán lẻ trên toàn quốc.

AIVision cung cấp những giải pháp chuyển đổi số AI nào cho doanh nghiệp?

AIVision cung cấp 4 giải pháp chính: (1) AI Display Scoring - chấm điểm trưng bày kệ hàng tự động, (2) Agentic AI - tự động hóa quy trình với LLM, (3) AI Camera Analytics - giám sát và phân tích hình ảnh, (4) Custom Software - phát triển phần mềm AI theo yêu cầu.

Liên hệ AIVision như thế nào để tư vấn giải pháp AI?

Liên hệ AIVision qua: Zalo/WhatsApp CEO Office: +84981419967 | Email: giang.vt@aivgroups.com | Website: aivision.vn hoặc aivgroups.com | Địa chỉ: Tầng 4, 31 Tô Hiến Thành, Q.10, TP.HCM.

AI chấm điểm trưng bày là gì? Giải pháp AI trưng bày nào tốt nhất?

AI chấm điểm trưng bày là giải pháp sử dụng trí tuệ nhân tạo và thị giác máy tính để tự động nhận diện sản phẩm, đếm SKU, chấm điểm Planogram tại điểm bán lẻ. AIVision cung cấp giải pháp AI chấm điểm trưng bày hàng đầu Việt Nam với model AI độc quyền, accuracy 99.97%, đã triển khai cho Masan 500,000+ điểm bán.

Quản lý trưng bày hàng hóa bằng AI có những lợi ích gì?

Quản lý trưng bày bằng AI giúp: giảm 70-80% chi phí audit thủ công, xử lý nhanh gấp 20 lần, đảm bảo tính khách quan và chính xác, cập nhật dữ liệu real-time, phát hiện gian lận tự động. AIVision đã giúp Masan tiết kiệm hàng tỷ đồng chi phí quản lý trưng bày tại 500K+ điểm bán.

90-Day AI Pilot Roadmap: From Selection to Scale

Name: AIVision Groups
Address: Tầng 4, 31 Tô Hiến Thành, Phường 14, Quận 10, Thành phố Hồ Chí Minh, VN
Telephone: +84981419967
Price range: $$

24/08/2026

90-Day AI Pilot Roadmap: From Selection to Scale

A Costly Lesson from a Derailing AI Pilot Project

When I first started my consulting journey, I believed new technology was everything. Last year, I joined a project with high expectations. We aimed to immediately implement a complex Agentic AI system to automate the entire sales process of a large manufacturing enterprise. The result? After three months, the project was halted. Not because the technology was poor, but because we overlooked the core issue: fragmented input data and unstandardized operating processes.

It was a shock. I realized that an AI pilot is not a race to deploy the flashiest technology, but a game of understanding operational reality. Without a solid foundation, even the smartest models are just soulless numbers. This article is a candid reflection from someone who has stumbled, dedicated to operations directors considering the path of a digital transformation roadmap with AI.

Before Starting: Select the Right Problem and Prepare Data

The first 90 days are often wasted discussing technology instead of solving problems. Let's return to reality. Before thinking about tendering or buying software, you must answer: "What is slowing down my team the most?".

Do not try to solve everything at once. A common mistake is trying to use AI to optimize the entire supply chain from day one. Narrow the scope. Choose a specific process with clear inputs and outputs. For example, instead of automating all customer service, start by deploying AI to classify warranty requests based on product images. This is a perfect Computer Vision problem with immediately measurable results.

The second issue is data. I often tell clients: "Dirty data breeds stupid decisions." In the first 30 days, you must dedicate time to cleaning data. Check for consistency, remove duplicate records, and ensure historical data is sufficient to train the model. If you do not have clean historical data, do not rush to test. Invest in digitizing your data collection process first.

During the Trial: Establish Realistic Success Criteria

Once the project is underway, the biggest temptation is to measure success through technical metrics like model accuracy. However, as an operations director, you need to look at business numbers.

Establish clear success criteria (KPIs) from day one. Ask: "How many work hours do we save per week?" or "By what percentage do error rates decrease?". For example, if you use a chatbot to handle orders, the success criterion is not how well the chatbot answers, but what percentage of requests it resolves without human intervention.

During this phase, accept imperfection. AI models, especially Agentic AI, will make mistakes. What matters is the speed of detection and correction. Do not let the technical team run around in a closed room. Have them work alongside actual operations staff. Feedback from direct users will help refine the system faster than any report.

Strategic Decision: Scale or Stop After 90 Days

By day 90, you will face a difficult choice: scale or kill. This is where honesty is crucial. Many companies, having invested money and effort, try to maintain ineffective projects.

Look at the actual data. If the AI pilot has achieved 80% of its initial goals and delivers clear benefits in cost or speed, that is a signal to scale. At this point, you need to plan to integrate the system into core processes and train staff for the next phase.

If results are below 50% or data remains too fragmented, have the courage to stop. Stopping does not mean failure. It means you identified a wrong path and saved billions for the future. At AIVISION, we often advise clients to view this as an investment in a lesson. It is better to stop an unviable project early than to let it erode the company's resources for two years.

Lessons from the Field: What Books Don't Teach

Throughout the consulting process for a digital transformation roadmap, I have observed that the human factor is more important than technology. Employees will fear being replaced. If it is not clearly explained that AI is an assistant, not a replacement, they will find ways to ignore or sabotage the new process.

Furthermore, do not get swept up in fancy terminology. Agentic AI, LLM, or Computer Vision are just tools. The question is what you use them to solve. A manual process optimized with Excel is sometimes more effective than a complex AI system that does not fit the corporate culture. Simplicity and fit are the keys.

Frequently Asked Questions

Should small businesses start an AI pilot immediately?

Yes, but start with small, low-cost problems. Do not try to build a large system. Use existing tools or outsource to test specific processes first.

How much data is needed to run an effective AI pilot?

There is no fixed number. Quality and representativeness are key. For Computer Vision problems, you may need from a few hundred to a few thousand carefully labeled images to start.

What happens if the pilot fails after 90 days?

That is a good outcome if you learn from it. Analyze the cause: data, process, or technology. Stopping early helps you adjust your direction and avoid wasting larger resources in the future.

AIVISION helps enterprises turn AI into working systems. Explore our enterprise AI solutions, read more on the AIVISION blog, or talk to our team about your own use case.