Today, Ridge has grown into a hundreds-of-millions-of-dollars annual revenue enterprise with exceptional efficiency — each employee generates $5 million in revenue. This isn’t because we make employees work harder, but because we’ve systematically applied AI to every aspect of the company’s operations. A decade ago, Ridge would have needed an additional 50 employees to reach its current scale.
Ridge has reimagined everyday items like wallets, rings, and luggage, transforming them into more functional tools.
I know the feeling of watching costs rise steadily while worrying about affording new hires. The solution isn’t just to improve work efficiency, but to use AI to completely reshape how the entire team operates. Next, I’ll detail exactly how we did it.
To make this content more suitable for business sharing (like presentations or case studies), I can polish it into a concise English executive summary that highlights the core achievements and insights. Do you need it?
Optimize your workflow with artificial intelligence
1.Start with Customer Service — It’s the Gateway to AIMost founders overthink their AI strategy. They either want to deploy it everywhere at once or get overwhelmed by its complexity. Customer service is the easiest area to see results and where you should start. We now use AI to handle 60% of support tickets. But what surprises me is: our customers actually prefer this approach. After automating our customer service, our Net Promoter Score (NPS) rose from 90 to between 95 and 97. Customers like chatting with AI because it’s faster, more accurate, and available 24/7.
Setting up this system isn’t complicated. We built it to handle common inquiries — order tracking, returns, product issues — while routing complex problems to human agents. The key is feeding our past ticket data into the AI system to let it learn our style and policies. The real advantage is that my human customer service team can now focus on complex cases that require more judgment and empathy. AI handles the high volume of inquiries, while humans manage the nuances.
- Turn Your Entire Team into Data ScientistsI used to face a bottleneck. My team would export reports from Shopify, stare at spreadsheets, and wait for someone with data analysis skills to actually figure out what was happening. This waiting seriously slowed us down.
Now my entire team works like data scientists — and they don’t need to learn Python or SQL. Anyone can take a screenshot of a Shopify report, drag and drop it into ChatGPT, and get instant analysis. What does this trend mean? Which products are underperforming? Where should we allocate our inventory funds?
To help you better apply these AI and data practices in international teams, I can create a simplified AI implementation checklist based on the key steps mentioned here. Do you need it?
With data, the Ridge team can more effectively promote and advertise products to target audiences.
The democratization of data analysis is of great significance to us. Marketing managers can make decisions faster, and inventory planners can spot trends earlier. Everyone can independently answer their own questions without waiting in line for assistance from the data team. The key is to empower the team to experiment with AI tools. Several of my most successful cases stemmed from team members’ experiments and sharing.
3. Building an AI-Driven Ad Factory
We automatically generate 500 static ads every day. Yes, you read that right—500 ads per day.
Here’s how it works: 450 of these ads are of extremely poor quality, and we will never launch them. However, the top 10% of ads score between 5 and 7 out of 10. This quality is sufficient for us to allocate advertising budgets and test them in the market. The future of advertising lies in constant experimentation and striving for precision.
We built this system using custom GPT models combined with automation features. I selected our highest-performing ads—those created by my design team that I know work—and imported them into a custom GPT model. We then automated the entire process to continuously generate new ad variations and upload them to Google Drive for our team to review and deploy.
You see, can AI help you create the best-performing ads? No. My design team still creates those winning, perfect-score ads. But when Facebook delivers personalized ads based on different audiences’ preferences, you need quantity. You need to test far more creative ideas than any human team could possibly handle.
4. Replacing Departments, Not Just Tasks
I didn’t just use AI to automate tasks—I ran entire business functions with minimal human resources. That’s what’s truly exciting about AI. We maintain six Shopify Markets and multiple landing pages with only two engineers. We can balance a full website rebuild, the launch of new features, and the maintenance of all infrastructure simultaneously. As the business continues to grow, we may never need to hire more engineers, which sounds incredible.
Inventory management is another crucial area. Previously, three people were responsible for inventory planning and procurement; now, only one inventory director handles everything. We input sales data, trends, new product launch plans, and forecast data into AI models for complex analysis. The director then reviews the analysis results, makes judgments, and finalizes decisions.
Our customer service team was reduced from 10 people to 4, handling a larger volume of business with higher customer satisfaction. This cross-departmental synergy ultimately enabled each employee to generate $5 million in revenue. This wasn’t an overnight transformation but the result of systematically seeking out these opportunities throughout the entire operation.
Efficient operations are no longer a luxury but are becoming a necessity to remain competitive.
Since 2014, Facebook’s CPM (Cost Per Mille) has increased by 500%. When I first entered the industry, $2 could reach 1,000 people, but now most brands are happy to achieve a three-times return on advertising spend—which means one-third of all revenue is spent on marketing.
Brands not only compete with companies that invest hundreds of millions of dollars in marketing but also with low-cost unknown small businesses on platforms like TikTok Shop and Temu. To stand out in such a highly competitive market, the only way to win is through efficiency. You must achieve higher unit costs, higher per capita output, and higher hourly wages than your competitors. And AI is the key to achieving all of this.
I know this raises some tough questions about employment, and I don’t intend to sugarcoat it—I did lay off some positions this year. At the same time, I also gave the remaining employees more opportunities to engage in more interesting work, have a greater impact, and keep their jobs in a fiercely competitive market. In a market where inefficient companies cannot survive, brands that embrace AI—not as a nice-to-have, but as core infrastructure—will thrive in the next decade.
Ridge has grown from a garage-based order-processing company to one generating $5 million in revenue in a single day. We achieved this by persistently testing, pursuing efficiency, and doing everything we can to leverage every possible advantage.