AI labs are chasing a slice of the corporate pie in Southeast Asia

I’ve worked a lot with the Microsoft stack, and for a long time Excel was its staple. It gave Microsoft a foothold inside companies. Once a company ran its budgets, forecasts and operations in Excel, Microsoft could sell a lot of other products around it. It became embedded in the organisation. And after decades of Excel hegemony, I think we finally see a proper contender for that position in the corporate workspace, and that is Gen AI.

I don’t mean a chatbot will replace a spreadsheet. AI competes in a different sense: it is becoming the layer through which people read documents, analyse numbers, write software and search internal knowledge. Once it becomes part of daily work, vendors can build a large stack around it. Models are only the entry point.

This is why AI labs are trying to bite from the corporate pie. Subscriptions made them famous and APIs brought developers, but enterprise is where the contracts get larger, the relationships longer and the sales harder. Companies need somebody to integrate the models, handle security review, train people and stay when the first project gets stuck.

Anthropic is opening a Singapore office in October, but that is not the main thing. It is building a partner network, certification and a pool of people who can implement Claude. Anthropic is a hot cake. The queue is huge, and they are picky.

That network lets Anthropic extend its reach without building a large local team. Local partners understand the banks, airlines and government agencies in the region: who approves the budget, where the data can sit, which old system cannot be touched and why a technically simple integration can still take six months.

Also Read: Singapore’s AI dividend will depend on what happens after the pilot phase

OpenAI is developing its partner programme too, although Anthropic seems further along with the channel. We at VGTech work with OpenAI, and the structure is familiar: product learning paths, a sales framework and a route from product knowledge to customer deployment.

Microsoft did not become dominant by implementing every piece of software itself. It built an ecosystem that could sell, customise and support the stack almost anywhere: certifications, partner levels, sales material and thousands of consulting companies making its products work inside specific businesses.

AI labs need the same kind of ecosystem around their models. The product changes every few months, the use cases are still unstable and nobody has a 20-year implementation playbook. So this ecosystem is not simply a distribution channel. It is also how a lab learns what corporate customers actually need. A partner brings back the awkward problems that never appear in a model demo.

Then there is Mistral. It cannot outspend Microsoft, Google or Amazon, and it lacks OpenAI’s brand. Its angle is control: customisable models, private deployment and technological sovereignty. That appeals to governments and regulated industries, as its work with Singapore’s defence and public-safety organisations shows.

Singapore is the natural place to start: regional headquarters, government support, buyers who can fund experiments and technical talent. But the real market is the banks and public-sector organisations across APAC.

A new layer of corporate work is emerging, and we can see the race to own it. Models may open the door, as Excel once did, but they will not close the deal alone. That gives startups and small companies an opportunity to test products and acquire customers.

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