Thinking Machines Becomes OpenAI’s First APAC Partner

Artificial intelligence is poised to revolutionize Asia Pacific, and Thinking Machines Data Science is strategically positioning itself to lead the charge. In a significant move signaling the growing importance of AI in the region, Thinking Machines is partnering with OpenAI to help businesses translate AI investments into tangible results. This collaboration marks Thinking Machines as the first official Services Partner for OpenAI in APAC.

The partnership arrives at a time when AI adoption in APAC is experiencing rapid growth. While an IBM study indicates that 61% of enterprises in the region are already utilizing AI, many are struggling to scale beyond initial pilot projects and realize significant business value. Thinking Machines and OpenAI aim to bridge this gap by offering comprehensive solutions including executive training on ChatGPT Enterprise, support for developing custom AI applications, and guidance on integrating AI into core business operations. This holistic approach seeks to empower organizations to not only adopt AI but also to effectively leverage its capabilities.

Stephanie Sy, Founder and CEO of Thinking Machines, emphasizes the importance of capability building in this partnership: “We’re not simply introducing new technology. We’re empowering organizations to develop the skills, strategies, and support systems necessary to unlock the full potential of AI. Our focus is on fundamentally reinventing the future of work through seamless human-AI collaboration, ensuring that AI truly benefits individuals and businesses across the Asia Pacific region.”

Turning AI Pilots into Results with Thinking Machines

A key challenge for enterprises, according to Sy, lies in the very framing of AI adoption. Frequently, companies view AI as a mere technology acquisition rather than a comprehensive business transformation strategy. This limited perspective often leads to pilot projects that either stall or fail to scale effectively.

“The central issue is that many organizations approach AI as a technology purchase rather than a strategic business overhaul,” Sy explains. “This results in pilot programs that never achieve scalability because three fundamental elements are missing: strong leadership alignment on the value proposition, redesigned workflows that seamlessly integrate AI into existing processes, and investment in workforce skills to ensure widespread adoption. By addressing these three critical areas – vision, process, and people – organizations can successfully scale their AI initiatives and generate meaningful impact.”

Leadership at the Center

Many executives still regard AI as a technical project rather than a strategic imperative. Sy believes that leadership from boards and C-suites is essential to drive successful AI initiatives. Their role is to define whether AI is a growth driver or simply a risk to be managed and mitigated.

“Boards and C-suites establish the overall direction: Is AI a strategic enabler of growth or a risk that needs careful management? Their responsibility is to identify key priority outcomes, define the organization’s risk tolerance, and assign clear accountability,” Sy states. Thinking Machines often initiates engagements with executive sessions where leaders can explore the value proposition of tools like ChatGPT, establish governance frameworks, and determine the appropriate scaling strategy. “This top-down clarity is what transforms AI from a mere experiment into a core enterprise capability,” she adds.

Human-AI Collaboration in Practice

Sy frequently discusses “reinventing the future of work through human-AI collaboration.” She describes this as a “human-in-command” approach, where individuals focus on tasks requiring judgment, decision-making, and handling exceptions, while AI handles routine tasks such as data retrieval, drafting documents, or summarizing information. This division of labor maximizes efficiency and allows human employees to focus on higher-value activities.

“A human-in-command approach involves redesigning work processes to allow people to focus on critical judgment and exceptions, while AI automates retrieval, drafting, and routine tasks, all with transparency through audit trails and source links,” Sy explains. The resulting improvements are measured in both time savings and enhanced quality.

Thinking Machines’ workshops demonstrate that professionals using ChatGPT can often save one to two hours per day. This is supported by research, including an MIT study that showed a 14% productivity increase for contact center agents, with the most significant gains observed among less-experienced staff. “This provides compelling evidence that AI can enhance human talent rather than replace it,” Sy concludes.

Agentic AI with Thinking Machines’ Guardrails

Thinking Machines is also concentrating on agentic AI, which expands beyond simple queries to manage multi-step processes. Agentic systems can handle research, complete forms, and make API calls, orchestrating entire workflows with human oversight, moving beyond just answering questions.

“Agentic systems can shift work from a simple ‘ask-and-answer’ dynamic to a multi-step execution process, coordinating research, browsing, form-filling, and API calls, enabling teams to deliver faster results with human control,” Sy explains. Though promising faster execution and greater productivity, agentic AI introduces potential risks. “The principles of human-in-command and auditability remain paramount to avoid potential pitfalls. Our approach involves combining enterprise controls and auditability with agent capabilities to ensure actions are traceable, reversible, and aligned with policies before scaling.”

Governance That Builds Trust

While adoption accelerates, effective governance often lags, creating potential risks. Sy cautions that governance fails if it is treated as mere paperwork rather than an integral part of daily operations.

“We maintain human oversight and integrate governance visibly into daily work: using approved data sources, enforcing role-based access, maintaining audit trails, and requiring human decision points for sensitive actions,” she explains. Thinking Machines employs a “control + reliability” approach, restricting retrieval to trusted content and providing answers with relevant citations. Workflows are then adapted to comply with local regulations in sectors like finance, government, and healthcare, ensuring responsible AI implementation.

For Sy, success is measured not by the number of policies but by auditability and exception rates. “Good governance accelerates adoption because teams trust the outputs they produce,” she states.

Local Context, Regional Scale

The cultural and linguistic diversity of Asia Pacific presents unique challenges to scaling AI. A one-size-fits-all approach is not feasible. Sy emphasizes the importance of building locally first and then scaling strategically.

“Global templates often fail because they ignore how local teams operate. The successful strategy is to build locally and then scale deliberately: adapt AI to local languages, forms, policies, and escalation procedures; then standardize the aspects that are transferable, such as your governance framework, data connectors, and impact metrics,” she explains.

Thinking Machines has adopted this approach in Singapore, the Philippines, and Thailand, demonstrating value with local teams before expanding throughout the region. The aim is not to create a uniform chatbot, but rather a dependable pattern that respects local context while maintaining scalability.

Skills Over Tools

When asked about the most vital skills in an AI-enabled workplace, Sy emphasizes that skills, not just tools, are key to achieving scale. She identifies three critical categories:

• Executive literacy: The ability of leaders to define outcomes and guardrails, and to determine when and where to scale AI initiatives.
• Workflow design: The redesign of human-AI interactions, clarifying who drafts, who approves, and how exceptions are handled.
• Hands-on skills: Prompting, evaluation, and source retrieval from trusted sources to ensure that answers are verifiable rather than simply plausible.

“When leaders and teams share this foundation, adoption shifts from experimentation to repeatable, production-level results,” she says. Thinking Machines’ programs have shown that many professionals report saving one to two hours per day after just a one-day workshop. To date, over 10,000 people across various roles have been trained, and Sy notes the consistent pattern: “Skills combined with governance unlock scale.”

Industry Transformation Ahead

Looking ahead to the next five years, Sy anticipates AI shifting from drafting tasks to full execution in critical business functions. She expects significant gains in areas such as software development, marketing, service operations, and supply chain management.

“For the near future, we foresee three concrete patterns: policy-aware assistants in finance, supply chain copilots in manufacturing, and personalized yet compliant customer experiences in retail, all built with human checkpoints and verifiable sources to allow leaders to scale with confidence,” she explains.

A practical example is BEAi, a system Thinking Machines built with the Bank of the Philippine Islands. This retrieval-augmented generation (RAG) system supports English, Filipino, and Taglish, providing answers linked to original sources with page numbers and understanding policy supersession. This transforms complex policy documents into accessible guidance for staff. “That’s what ‘AI-native’ looks like in practice,” Sy concludes.

Thinking Machines Expands AI Across APAC

The partnership between Thinking Machines and OpenAI will initially focus on programs in Singapore, the Philippines, and Thailand through Thinking Machines’ regional offices, with plans for broader expansion across APAC in the future. The company intends to tailor services to specific sectors such as finance, retail, and manufacturing, where AI can address unique challenges and unlock new business opportunities.

Sy’s vision is clear: “AI adoption is not just about experimenting with new tools. It’s about developing the vision, processes, and skills that enable organizations to move from pilot projects to impactful results. When leaders, teams, and technology work together effectively, AI delivers lasting value.”