OpenAI Forges Key APAC Partnership with Thinking Machines

Thinking Machines Data Science is partnering with OpenAI to empower more businesses in the Asia Pacific region to transform artificial intelligence into tangible business outcomes. This collaboration establishes Thinking Machines as OpenAI's first official Services Partner in the region.

This alliance is timely, as AI adoption in APAC accelerates. According to an IBM study, while 61% of enterprises are already using AI, many find it difficult to progress beyond pilot stages and achieve meaningful business impact. Together, Thinking Machines and OpenAI will address this gap by providing executive training on ChatGPT Enterprise, support for developing custom AI applications, and guidance on integrating AI into daily operations.

Stephanie Sy, Founder and CEO of Thinking Machines, highlighted the partnership's focus on building capability: "Our mission goes beyond introducing new technology. We are equipping organisations with the essential skills, strategic frameworks, and support systems needed to harness AI effectively. We are dedicated to reshaping the future of work through human-AI collaboration, ensuring AI delivers real value for people across Asia Pacific."

From AI Pilots to Tangible Results with Thinking Machines

In a conversation with AI News, Sy identified a common obstacle for enterprises: their mindset towards AI adoption. Many organisations treat it as a simple technology purchase rather than a fundamental business transformation. This often results in pilot projects that stall or fail to expand.

"The core issue is that companies frequently approach AI as a tech acquisition, not a business transformation," she noted. "This leads to unscalable pilots because three critical elements are missing: strong leadership alignment on the desired value, a redesign of workflows to embed AI, and investment in upskilling the workforce. Get these three pillars—vision, process, and people—right, and pilots evolve into widespread impact."

Leadership at the Core

Executives often still view AI as a technical initiative rather than a strategic priority. Sy emphasises that boards and C-suites must set the direction. They need to decide if AI is a driver for growth or merely a risk to be managed.

"Leadership sets the tone: Is AI a strategic growth lever or a managed risk? Their role is to define priority outcomes, establish risk appetite, and assign clear ownership," she stated. Thinking Machines often starts with executive workshops where leaders explore where tools like ChatGPT create value, how to govern them, and when to scale. "This top-down clarity is what turns AI from an experiment into a core enterprise capability."

Human-AI Collaboration in Practice

Sy frequently discusses "reinventing the future of work through human-AI collaboration." She described this in practice as a "human-in-command" model, where people focus on judgment, decision-making, and handling exceptions, while AI manages routine tasks like data retrieval, drafting, or summarisation.

"A human-in-command approach means redesigning work so people concentrate on judgment and exceptions, while AI handles retrieval, drafting, and routine steps—all with transparency through audit trails and source links," she explained. The benefits are measured in time saved and quality enhanced.

In Thinking Machines' workshops, professionals using ChatGPT often reclaim one to two hours daily. Research supports this; Sy cited an MIT study showing a 14% productivity increase for contact centre agents, with the largest gains among less experienced staff. "This is clear evidence that AI can augment human talent, not replace it," she added.

Agentic AI with Built-in Guardrails

Another key focus for Thinking Machines is agentic AI, which manages multi-step processes beyond single queries. Instead of just answering a question, agentic systems can coordinate research, complete forms, and make API calls, orchestrating entire workflows while keeping a human in control.

"Agentic systems elevate work from simple Q&A to multi-step execution: coordinating research, browsing, form-filling, and API calls so teams deliver faster, with a human overseeing the process," Sy said. While the promise is greater speed and productivity, the risks are real. "The principles of human oversight and auditability remain critical to prevent a lack of proper guardrails. Our approach integrates enterprise controls and auditability with agent capabilities, ensuring all actions are traceable, reversible, and aligned with policy before scaling."

Governance That Builds Trust

While AI adoption speeds ahead, governance often lags. Sy warned that governance fails when treated as mere paperwork instead of being integrated into daily work.

"We keep humans in command and make governance visible in daily operations: using approved data sources, enforcing role-based access, maintaining audit trails, and requiring human approval for sensitive actions," she explained. Thinking Machines also applies a "control + reliability" framework, restricting retrieval to trusted content and providing answers with citations. Workflows are then adapted to meet local regulations in sectors like finance, government, and healthcare.

For Sy, success is measured not by the number of policies but by auditability and low exception rates. "Effective governance actually accelerates adoption because teams trust what they deploy," she said.

Local Context, Regional Scale

Asia Pacific's cultural and linguistic diversity presents unique challenges for scaling AI. A universal model rarely works. Sy stressed that the proven strategy is to build solutions locally first, then scale them deliberately.

"Global templates fail when they ignore local workflows. The right playbook is to build locally and scale deliberately: tailor the AI to local language, forms, policies, and escalation paths; then standardise the elements that travel well, like your governance framework, data connectors, and impact metrics," she stated.

This is the approach Thinking Machines has taken in Singapore, the Philippines, and Thailand—demonstrating value with local teams first, then expanding regionally. The goal isn't a uniform chatbot, but a reliable, repeatable pattern that respects local context while ensuring scalability.

Skills Over Tools

When asked about the most important skills for an AI-enabled workplace, Sy asserted that sustainable scale comes from skills, not just software. She outlined three key categories:

• Executive Literacy: Leaders' ability to define outcomes, set guardrails, and know when and where to scale AI.
• Workflow Design: Redesigning human-AI handoffs, clarifying who drafts, who approves, and how exceptions are escalated.
• Hands-on Skills: Effective prompting, evaluation, and retrieving information from trusted sources to ensure answers are verifiable, not just plausible.

"When leaders and teams share this foundational understanding, adoption moves from experimentation to repeatable, production-level results," she said. In Thinking Machines' programs, many professionals report saving one to two hours daily after just a one-day workshop. To date, over 10,000 people across various roles have been trained, with a consistent pattern emerging: "skills + governance unlock scale."

Industry Transformation Ahead

Looking ahead five years, Sy sees AI evolving from a drafting aid to fully executing critical business functions. She anticipates significant gains in software development, marketing, service operations, and supply chain management.

"For the next wave, we see three concrete patterns: policy-aware assistants in finance, supply chain copilots in manufacturing, and personalised yet compliant customer experience in retail—each built with human checkpoints and verifiable sources so leaders can scale with confidence," she projected.

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, provides answers linked to sources with page numbers, and understands policy updates, turning complex documents into practical staff guidance. "That's what 'AI-native' looks like in practice," Sy remarked.

Thinking Machines Expands AI Across APAC

The partnership with OpenAI will launch initially through Thinking Machines' offices in Singapore, the Philippines, and Thailand, before expanding across APAC. Future plans include tailoring services for sectors like finance, retail, and manufacturing, where AI can solve specific challenges and unlock new opportunities.

For Sy, the objective is clear: "AI adoption isn't just about experimenting with new tools. It's about building the vision, processes, and skills that enable organisations to move from pilots to lasting impact. When leadership, teams, and technology align, that's when AI delivers enduring value."

See also: X and xAI sue Apple and OpenAI over AI monopoly claims

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