Paulo: Thanks again for taking the time to come on. As you’ve seen on our show, we’ve had a couple of your colleagues on already. Obviously, Aaron, and then Ernest as well—twice, I think. And then Bryan—we even did a whole documentary. I flew over to Carro Indonesia, and filmed him going around. I’m happy to have another member of the management team here to get another perspective on the company. I think it was just a week or two ago when I reached out, and we were able to set this up—really because of that LinkedIn post I saw. I had just come from an AI event with some of our other portfolio companies, so the topic was fresh in my mind.

Then I saw your post, the Google Cloud post, and how you reposted it. That’s when I thought, “Oh, here’s someone I can talk to who has actually gone through this whole journey—not just someone who started using Gen AI last month, but someone who has been experimenting, failing, and learning over several years.” I think it would be really interesting to hear more about that on the show.

So I thought we’d start off by giving some context on how you even ended up pushing this initiative within Carro. Maybe first, let’s talk about how you joined Carro—how you found out about the company, given that you’ve been in several tech companies before. What was that journey like for you?

How Zi Yong joined Carro after PayPal, Alipay, and his own ventures

Zi Yong: Thanks for having me. It’s a pleasure to be here with you. As you mentioned, many of my colleagues have been on this podcast before.

I don’t tend to post much on LinkedIn, but quite a number of things have happened in the last year in terms of solutions being rolled out into production. Google Cloud wanted to work with us to showcase some of these solutions, which is why I posted that article two weeks ago. I think that’s how you picked it up.

Happy to share from an operator’s perspective how we built our Gen AI journey from the ground up. We’re not trying to build AI for AI’s sake—we’re really trying to get business value out of it. It stems from my role because I’m in charge of operations and processes. It’s really about how we streamline our people and processes, using Gen AI to facilitate that.

Thanks for the quick introduction. Before joining Carro, I actually spent more than ten years in internet companies. In fact, I started my own company about 15 years ago.

Back when Android had just been released. In fact, I developed the first Android geolocation app for Singapore back in December 2008. After that, I ran a mobile payment company, then moved on to PayPal and later Alipay, where I was leading various regional projects and digital initiatives.

At Alipay, I was supporting Paytm and Touch ‘n Go, helping develop their super apps. You probably know that Paytm is the largest wallet in India, and TNG is the largest wallet in Malaysia. I was part of the product team driving digital transformation for those companies in those markets.

I joined Carro because I knew Aaron back when I was running my own company. At the time, he was with Singtel Innov8, which was also one of my investors. I was always impressed by Aaron as a person, and we interacted frequently because of our investor relationship.

When I left, I told him that whatever he started in the future, I’d be more than happy to invest in it. So I actually personally invested in Carro’s Series A. And by the way, I didn’t even know what the company did! It didn’t matter—I was betting on Aaron.

Fast forward to 2020, at the height of COVID. Aaron and I caught up again, and he told me he needed someone to help him at Carro. By that time, I had spent seven or eight years in the corporate world and wanted to go back to startups. So I signed up and joined Carro.

Paulo: That’s an interesting journey. Typically, people join big tech before they start their own company, but you did it in reverse. I love the story about him investing in your company, and then you eventually investing in Carro early on.

You mentioned that you’re in the COO role, and I really like how the PPT framework sums up what you do in a concrete way. Maybe you can take us on this journey—you’ve been with Carro since 2021, right? That’s already four years now.

Quite some time in startup years. So there’s definitely a lot that you’ve seen and shaped within the company. Maybe you can walk us through that journey. Before this call, you mentioned splitting it into two parts. The first part was about “cleaning house”—maybe you can speak more about that?

Zi Yong’s first order of business as COO, refining Carro’s internal data processes for better productivity

Zi Yong: When I first joined, my focus was on cleaning house and driving digital transformation. Carro had a lot of systems and tools—our own in-house ERP, our own CRM. Brian, whom you’ve met, built the AI pricing and inspection app that we use.

But truth be told, there were still many gaps in the workflow. Ground staff were using Google Sheets and Excel to plug the various workflow gaps. As a result, data going into our systems—whether ERP or CRM—was often inconsistent.

I remember having to do a lot of reconciliation for analysis and reporting. It was a nightmare—one single analysis would take me four weeks because I had to reconcile data across four or five different sheets, cleaning definitions across all business lines. Revenue in Malaysia was defined differently from revenue in Singapore, or in insurance, or in financing.

Back then, every country spent at least one day a week just generating PowerPoint reports. It was incredibly unproductive, and the numbers weren’t always reliable. My first priority was to clean house—move all workflows into ERP and CRM, standardize reporting across the group, and implement a governed data definition framework.

That meant clearly defining revenue with and without commissions, COGS, OPEX—everything had to be properly structured. This took about two years, going business unit by business unit, country by country.

Today, I can go into my dashboard and instantly get detailed slices of inventory, cohort profitability, and sales performance down to the individual salesperson in a specific branch in Malaysia.

So that set the stage for us to leverage this data and integrate it into Gen AI, allowing us to automate parts of our workflows as well as our alerts. Since we had already cleaned our data structure and built semantic mappings across all our metrics and dashboards, we could now focus on AI-driven automation.

How Zi Yong linked productivity gains to end business results

Paulo: Data is really the foundation for any kind of AI adoption. After two years of cleaning house and addressing data complexity and granularity, what came next? What was the second phase?

Zi Yong: By the end of my first year at Carro, I was tasked with turning around one of our biggest business units. At the same time, I was still in the midst of digital transformation—aligning all the systems, making sure every policy definition was clear. And suddenly, I had to step in to fix this BU.

But what was great about this challenge was that it gave me a proof point—showing that digital transformation actually brings tangible business results. Many organizations struggle with digital transformation because they don’t see the immediate purpose of data cleaning or having a well-governed data structure. CIOs will emphasize its importance, but at the end of the day, business priorities often take precedence.

For me, this was an opportunity to run both initiatives in parallel—driving transformation while also executing a business turnaround. My approach was to go into every single department within that BU, redefine people’s roles and responsibilities, and optimize the processes.

As I mentioned earlier, PPT—People, Process, and Technology—is the foundation. Ideally, I always start with the first P, which is People. It’s about ensuring that we have the right people in place, that they understand their roles, and that they know the KPIs they need to deliver. Once that foundation is in place, we move on to Process—ensuring that departments work seamlessly together, SOPs are clear, and everyone understands their handoff points.

It took one or two months to figure all of this out. Within a month, we were already able to track the performance of every salesperson, which ties back to what I mentioned earlier about productivity and tracking. By being able to measure performance at the individual, branch, and BU level, we could take immediate actions and make more informed decisions in real time.

For a company like Carro, this is absolutely critical. While we often talk about AI, retail experience, and delivering a seamless car ownership experience, the core of our business actually lies in supply chain management.

How efficient are we at procuring the right cars at the right price? How well do we manage costs across logistics, maintenance, and reconditioning? How fast can we sell these cars?

The more effectively we operate—whether in terms of time or cost—the greater our competitive advantage when going to market to procure cars. Because we can analyze what we buy, how fast a car turns over in our ecosystem, and what pricing strategies maximize profitability.

Paulo: So it’s about optimizing the car’s turnaround within the ecosystem.

Zi Yong: Exactly. As I always say, we are not in the business of selling one product a million times. We are in the business of selling a million products one time. Each car is unique. Okay, maybe not just one time—some cars might be resold two or three times in their lifetime—but you get the idea.

The real challenge is: How do we best manage all these cars?

This also ties back to Gen AI, because at any given time, Carro has tens of thousands of cars across our leasing books, marketplace inventory, and auction yards. We also manage cars that we procure for auction from banks.

How do we optimize inventory management in the most timely and cost-effective way to maximize value? This is a massive challenge, and it’s impossible for a human to manually analyze dashboards and make the best decisions for thousands of cars.

That’s where Gen AI comes in.

Zi Yong’s three principles on leveraging Gen AI

Paulo: The way you structured Part 1 and Part 2 really highlights how you linked efficiency in input metrics to efficiency in output metrics—from improving internal productivity to directly impacting the business’s bottom line.

That sets the stage perfectly for our discussion on Carro’s Gen AI journey. But before we dive into that, I think it’s important to understand your own views on Gen AI—because those perspectives shape how you lead your team, implement experiments, and drive adoption.

Zi Yong: To qualify myself—I’ve never been an engineer. In fact, my only background in programming was an introduction to programming course in C.

So, when I first built my Android app, it was in Java. I also wrote several applications for Google Glass. But here’s the thing—I built my apps using code snippets from Stack Overflow.

My superpower was knowing how to find the right functions, put them together, and make them work.

So when Gen AI came along, it was like a dream come true for me. Instead of me searching Stack Overflow for the right code snippets, Gen AI can now generate the code directly for me.

If Gen AI had existed 10 years ago, I would have been able to do so much more.

Now, looking at the broader picture, Gen AI is going to fundamentally change how work is done.

1. Smaller teams with cross-domain expertise.

Instead of large teams of specialized experts, we’ll see small, agile teams with broad knowledge across multiple domains.

The key will be integrating solutions rather than just specializing in one area.

2. Bottom-up innovation instead of top-down mandates.

In the past, AI projects were often driven by data scientists or engineers, trying to build solutions and then hoping the business would use them.

With Gen AI, the model flips—the best solutions will come from business owners who have full accountability for results.

Instead of AI teams dictating strategy, they become partners, helping business teams bring ideas to life.

3. Quick wins over big, complex projects.

Rather than spending years building a massive AI-powered system, companies should focus on small, high-impact tools that deliver immediate value.

Let me give you an example—document processing for loan approvals.

At Carro, we want to provide the best experience for customers applying for financing. The process involves assessing a customer’s credit score and determining whether to approve their loan.

Unlike Singapore, many countries don’t have SingPass, so a lot of documents come in paper form. This means huge teams of people manually entering data into systems—whether at Carro or at a bank.

Instead of building a massive AI system, we focused on solving the front-end problem:

• How can we convert documents into a machine-readable format (JSON)?

• How can we automate data entry to speed up credit approvals?

Once we solved that, the credit team could process applications in real time instead of taking days.

This simple solution reduced loan application time from 40 minutes to just 5 minutes.

We built this in less than a week—one day to prototype, five days to refine the UI, and we shipped it immediately. Our users have been using it ever since.

This is the power of quick wins—small tools that deliver immediate impact, rather than waiting years for a large-scale solution.

Paulo: That’s fascinating. We’re going to see more and more leaders like you—people without an engineering background—being able to build these kinds of AI-powered solutions without a massive engineering team.

Stay tuned for part 2 on how these principles impacted Carro’s Gen AI journey and the ROI of their Gen AI transformation

Paulo: How did your leadership principles translate into Carro’s Gen AI strategy?

Zi Yong: To give a quick preview—our journey started in early 2023, right around the time ChatGPT launched. But even before that, we were already seeing a lot of AI projects emerging, like LangChain…