SpecialtyNetworkSllc – Artificial intelligence is transforming the way businesses operate—but turning AI potential into real impact remains a challenge. That’s exactly what Thinking Machines Data Science and OpenAI aim to change through a landmark partnership announced earlier this year. As OpenAI’s first official Services Partner in Asia Pacific, Thinking Machines is setting a new standard for how enterprises adopt and scale AI solutions.
AI adoption is growing rapidly across Asia Pacific. An IBM study recently reported that 61% of enterprises in the region are already using AI. But many are stuck in the “pilot phase,” unable to translate experimental projects into tangible business results.
Gail Sy, Founder and CEO of Thinking Machines, highlights why these initiatives often stall: “Most companies see AI as just a tech upgrade. But to succeed, you need to treat it as a business transformation.”
According to Sy, three core ingredients turn pilots into scalable solutions:
With OpenAI, Thinking Machines provides executive training for tools like ChatGPT Enterprise, builds custom AI applications, and helps embed AI into everyday business operations.
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Sy emphasizes that successful AI adoption starts at the top. “Boards and C-suites need to define what AI means for their company — is it a growth strategy or a managed risk?” she said. Thinking Machines hosts executive workshops to help leaders identify use cases, define governance, and set a roadmap for scaling AI.
This clarity from leadership becomes the cornerstone of adoption. “Top-down alignment is what turns AI from a flashy experiment into a long-term business capability,” Sy added.
At the heart of Thinking Machines’ philosophy is human-in-command AI — an approach where AI enhances, not replaces, human judgment. Instead of trying to automate everything, the company designs workflows where humans manage complex decisions and exceptions, while AI handles tasks like drafting, summarization, and information retrieval.
“AI saves time and improves quality when you pair it with transparency and human oversight,” Sy explained. In workshops, professionals often report gaining back one to two hours per day using tools like ChatGPT.
This isn’t just anecdotal. A recent MIT study found a 14% productivity boost among contact center agents using AI, especially for less experienced workers.
Thinking Machines also focuses on the next frontier of enterprise AI: agentic systems. These go beyond simple Q&A and can handle multi-step tasks like researching, filling forms, or triggering API calls — all while keeping humans in charge.
“Agentic AI moves work from ask-and-answer to full execution,” said Sy. “But it must come with enterprise controls, auditability, and human decision points to maintain trust and policy compliance.”
Their design philosophy ensures that every AI action is traceable, reversible, and aligned with business rules.
While AI tools become more powerful, governance often lags behind. Sy warns that governance frameworks must be integrated into everyday operations, not treated as an afterthought or paperwork exercise.
Thinking Machines uses a “control + reliability” model, which includes:
This approach adapts to local regulations, especially in finance, government, and healthcare sectors. “Good governance builds trust — and trust accelerates adoption,” said Sy.
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Asia Pacific’s diversity in language, culture, and regulation presents unique challenges. A global AI template simply doesn’t work here.
“Our strategy is to build locally, then scale,” Sy noted. Thinking Machines has already applied this model in Singapore, the Philippines, and Thailand, proving value with local teams before expanding regionally.
This ensures AI aligns with local languages, workflows, and policies, while still using scalable templates for governance, data connectors, and metrics.
Sy believes that tools alone won’t drive transformation — skills will. She breaks the must-have capabilities into three areas:
With these capabilities, organizations can move from experimentation to production-level impact. To date, over 10,000 professionals have completed Thinking Machines’ AI training programs — and many report saving 1–2 hours daily.
