Is Your AI Strategy Built on a Broken Data Foundation?

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Welcome back to The Asia Tech Podcast. Today we discussed why so many APAC enterprises are funding AI before their data foundations can support it, what agentic AI actually costs once the hype wears off, and why “digital sovereignty” might be a much bigger problem than most companies realize.

• Michael Barnes - Chief Analyst, Omdia

• David Irecki - CTO, APJ, Boomi

Here is an overview of the topics we discussed:

Why do companies pour money into AI before they’ve built the infrastructure to support it? Michael argues it’s not actually unusual — most big tech shifts start with a proof of concept, not a platform strategy. Companies chase a use case, prove it works, and only then start asking hard questions about governance and scale.

The problem is what happens next. David points out that once a pilot succeeds, teams try to scale it and discover their data foundations were never built for enterprise-wide use. That gap between “it worked in the pilot” and “it works everywhere” is where a lot of AI budgets quietly blow up.

Is agentic AI actually cheaper than a human employee? Not always, it turns out. David recalls a business owner who found that for some tasks, a human was still the cheaper option once you accounted for the infrastructure agents need to run.

That’s forcing companies to get more selective about which model does which job — using frontier models for complex reasoning, and cheaper open-weight models for simpler tasks. The two guests dig into why open-weight models are catching up fast, and what that means for anyone still assuming bigger is always better.

If every company’s AI is trained on roughly the same internet data, how does anyone actually stand out? Both guests agree the answer is proprietary context — connecting your own internal data into the model so it produces answers no competitor’s AI can replicate. Get that “data liquidity” right, David says, and it becomes a genuine competitive wrapper around whatever model you use.

Just when companies think they’ve handled cloud, integration, and governance, along comes the sovereignty question — where is your data actually stored, processed, and does it cross borders it shouldn’t? Michael calls it a “rich vein” for analysts precisely because nobody fully understands it yet.

The conversation gets into why full sovereignty is nearly impossible for most companies, why countries like France are building their own AI to avoid depending on foreign tech, and how on-device AI could complicate the picture even further.

How do you actually control what an AI agent can access and do inside your company? One idea gaining traction: give agents an employee ID, tie their access to identity controls just like a human hire, and monitor them the same way — including “firing” them if they go off the rails. It’s a striking reframe of governance, and the guests explore how far that analogy can really stretch once agents start operating with real autonomy.

Some countries in the region are moving noticeably faster on AI than others — but why? The guests break down why Singapore and Malaysia are punching above their weight, driven by ambitions to become regional AI hubs, while other markets are still working through basic legacy system modernization before they can even think about AI at scale.

Want the full conversation, including why “100% autonomous agents” might be a red herring and what a future of smaller, more specialized AI models could look like?

Watch the full episode here:

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