Beyond the Model: How AI Labs Are Building Corporate Partnerships in Southeast Asia

Throughout my experience with the Microsoft ecosystem, Excel has long been the cornerstone application. It served as Microsoft’s beachhead within enterprises: once an organisation managed its budgets, forecasts, and operations within Excel, the company was positioned to sell a broad suite of adjacent products. Over decades of spreadsheet hegemony, I believe we are now witnessing a genuine rival emerge for that central position in the corporate workspace—generative AI.

This is not to suggest a chatbot will simply supersede the spreadsheet. Rather, AI competes on a different axis: it is becoming the interface through which employees read documents, interpret data, generate code, and retrieve internal knowledge. Once embedded in daily workflows, vendors can construct an entire stack around it. The models themselves are merely the entry point.

This dynamic explains why major AI labs are vying for a share of enterprise spending. Subscriptions built their fame and APIs attracted developers, but the enterprise sector offers the largest contracts, the longest relationships, and the most complex sales cycles. Corporations require partners who can integrate models, navigate security reviews, train staff, and remain committed when early initiatives stall.

Anthropic, for instance, is establishing a Singapore office this October. However, the office itself is secondary to its strategy: it is cultivating a partner network, certification scheme, and talent pool capable of implementing Claude. Anthropic is currently in high demand, with a substantial waitlist and selective onboarding.

This network allows Anthropic to expand its footprint without maintaining a massive local team. Regional partners possess the necessary context regarding banks, airlines, and government agencies: who holds the budget authority, where data residency requirements dictate storage, which legacy systems are untouchable, and why a technically straightforward integration can still span six months.

OpenAI is also developing a partner programme, though Anthropic appears further advanced in channel development. From my perspective at VGTech, where we work with OpenAI, the structure is recognisable: product learning paths, a sales framework, and a clear route from technical competence to customer deployment.

Microsoft did not achieve dominance by implementing every piece of software itself. It built an ecosystem capable of selling, customising, and supporting the stack almost anywhere—featuring certifications, partner tiers, sales collateral, and thousands of consulting firms ensuring its products functioned within specific business contexts.

AI labs require an equivalent ecosystem surrounding their models. With products evolving every few months, use cases still in flux, and no established twenty-year implementation playbook available, this ecosystem is more than a distribution channel. It is also the mechanism by which a lab learns what corporate customers actually require. Partners return with the awkward, real-world problems that never surface in a model demonstration.

Then there is Mistral. Unable to match the spending power of Microsoft, Google, or Amazon, and lacking OpenAI’s brand recognition, its strategy centres on control: customisable models, private deployment, and technological sovereignty. This appeals strongly to governments and regulated industries, as evidenced by its work with Singapore’s defence and public-safety organisations.

Singapore is the natural launchpad—offering regional headquarters, government backing, buyers able to fund experiments, and technical talent. Yet the broader market lies with the banks and public-sector institutions across APAC.

A new layer of corporate work is emerging, and the race to define it is already underway. Models may open the door, much like Excel did, but they will not close the deal alone. This environment creates an opportunity for startups and smaller companies to test products and acquire customers.

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