Thinking Machines Data Science has been named OpenAI’s first official Services Partner in the Asia Pacific (APAC) region, a collaboration designed to help enterprises move beyond experimental AI projects and turn them into measurable business outcomes.
The partnership comes as AI adoption across APAC accelerates. An IBM study found that 61% of enterprises in the region already use AI, yet many struggle to scale beyond pilot programs. Through this collaboration, Thinking Machines and OpenAI will offer executive training on ChatGPT Enterprise, support for building custom applications, and guidance on integrating AI into daily operations.
From Pilots to Impact
Stephanie Sy, Founder and CEO of Thinking Machines, emphasized that the real challenge isn’t technology—it’s transformation. Too often, she explained, companies treat AI as a tool purchase rather than a business reinvention.
“The main challenge is that many organisations approach AI as a technology acquisition rather than a business transformation,” Sy said. “Without leadership alignment, redesigned workflows, and workforce skills, pilots stall. With vision, process, and people in place, pilots scale into impact.”
To drive this shift, Thinking Machines begins with executive workshops that help boards and C-suites position AI as a strategic growth driver. These sessions define priorities, establish governance, and identify areas where AI like ChatGPT can deliver value.
Human-AI Collaboration in Practice
Sy advocates a “human-in-command” approach, where AI takes on repetitive tasks such as information retrieval, drafting, and summarisation, while humans focus on judgment and decision-making. In workshops, professionals using ChatGPT often free up one to two hours per day.
Research supports these gains. An MIT study found a 14% productivity boost for contact center agents using AI, with the biggest benefits seen among less-experienced staff.
Brian Eastwood
“That’s clear evidence AI can elevate human talent rather than displace it,” Sy said.
Thinking Machines is also exploring agentic AI, which manages multi-step processes such as research, form-filling, and API calls. These systems promise faster workflows but require strong governance.
“Our approach is to pair enterprise controls and auditability with agent capabilities to ensure actions are traceable, reversible, and policy-aligned before we scale,” Sy explained.
Governance and Local Context
Trust and governance remain central to adoption. Thinking Machines enforces role-based access, audit trails, and approved data sources, ensuring transparency in sensitive workflows. Its approach—“skills + governance unlock scale”—has already trained over 10,000 professionals across the region.
Sy also underscored the need for local context.
“Global templates fail when they ignore how local teams work. The playbook is build locally, scale deliberately,” she said.
The firm has applied this model in Singapore, the Philippines, and Thailand, tailoring systems to local languages and policies before rolling them out region by region.
Looking Ahead
Over the next five years, Sy expects AI to expand from drafting support to full-scale execution in industries such as finance, manufacturing, and retail. She points to projects like BEAi, a system built with the Bank of the Philippine Islands, which provides policy-aware answers in English, Filipino, and Taglish—an example of “AI-native” tools built for real-world impact.
The partnership with OpenAI will begin with programs in Singapore, the Philippines, and Thailand before expanding further across APAC.
For Sy, the goal is clear: “AI adoption isn’t just about experimenting with new tools. It’s about building the vision, processes, and skills that let organisations move from pilots to impact.”
