How AWS is powering the next-gen data era

How are AI and data trends impacting Vietnamese businesses, and how does this compare with other markets?

ASEAN in general has a remarkable rate of technology adoption because the population skews very young. And when I think about Vietnam specifically, the first thing I would highlight is the fintech sector. Vietnamese banks have been very forward-thinking in how they use data. Techcombank is a great example, it is one of the leading digital banks in Vietnam, and it is very advanced in how it thinks about data.

There’s a distinction between a fintech customer like Techcombank, and we have several others in Vietnam as well, versus other types of organisations. For fintechs and banks, adopting data and AI is a necessity. They have massive volumes of data, and it is literally the lifeblood of their business. Data is a core part of their backbone.

In Vietnam more broadly, you're also seeing strong adoption at what I call the 'consumption layer', where AI is built into the experience. For example, there has been a lot of adoption of QuickSight or Quick Suite, as it’s evolving. QuickSight became very popular in Vietnam because it allows users to create dashboards directly from natural language. And in that scenario, customers often don’t even realise they’re using AI, but they are. They use it through the natural-language interface without needing deep technical skills.

I really see two strong trends in Vietnam. The fintech sector, which is aggressively adopting AI and data because it’s fundamental to how they run their businesses. And the consumption layer of data, where AI-powered dashboards and user experiences are being adopted rapidly because they’re easy to use and deliver immediate value.

That combination is quite aligned with what we’re seeing in other fast-growing markets, high adoption, strong appetite for innovation, and a willingness to leverage AI wherever it simplifies and accelerates how people interact with data.

As businesses become increasingly data- and AI-driven, which core skills do teams need most, and how can AWS support them in building these capabilities?

When we think about our AWS services, our primary goal is to make both implementation and skill development effortless. Having deep, native integration built in is incredibly important. For example, if you're using vectors, you can use them today with Bedrock Knowledge Bases – it's built in. If you want to query S3 tables with SQL, you can easily do that using Redshift, Athena, or any of our analytics engines.

The key point is that the integration is already there. You don't have to learn a brand-new skill; you can simply use what you already know, and the AWS service handles the integration behind the scenes. That's the first piece.

The second piece is all the building blocks for agentic infrastructures. If you're a builder, those building blocks are incredibly powerful. But if you're not a builder, and you still want the benefits of agentic infrastructure, we recommend two services. The first is Amazon Connect, which has a very extensive agentic infrastructure operating under the hood.

The second is Amazon QuickSuite, launched in October, and customer feedback has been extremely positive. It includes Quick Dashboards, Quick Knowledge Bases, and more. They've also built a set of data connectors that let you plug into Office 365 and many other data sources. The work you do with your different datasets is all managed through an agentic infrastructure underneath the Quick experience.

This is how you can take advantage of agentic infrastructure without having to build it yourself – you use AWS services that already have it built in and understand where your data lives. One of QuickSuite's biggest strengths is its wide ecosystem of data connectors. You simply implement your connector, and then you can bring all your data together in the consumption layer, the user experience layer, while all the production-side complexity runs under the hood in QuickSuite.

With AWS introducing Agentic AI through services such as Amazon Bedrock AgentCore, how do you see autonomous agents transforming cloud, data services, and enterprise workflows over the next 2–3 years?

I actually think that the majority of data usage in the future will be driven by AI. And the reason is simple – agents never sleep. They work 24/7 and autonomously pursue a result or a goal. If you imagine one AI agent, or a hundred, constantly querying data around the clock, that’s an enormous amount of interaction with data.

That’s the world that’s emerging. As more agentic infrastructures and more agents start working on data simultaneously, this will only accelerate. And there’s no better place for that kind of interaction than S3, because of its scale. You want these interactions happening on your most scalable, cost-efficient systems.

This shift is also going to accelerate cloud adoption because doing this on on-premises data stores is neither feasible nor cost-effective. All that data needs to be in the cloud to give AI agents a place to operate.

And all of these AI coding assistant tools already interact with S3, Kiro does, AgentCore does, all our technologies do. So this idea of a 'data ocean' becomes essential because that’s what your AI agents will interact with. And having that data ocean on S3 is even more critical because S3 has the scale and economics to support the level of continuous interaction that a world of AI agents will require.

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