Thinking Machines and OpenAI Partner to Drive APAC-Wide AI Adoption, Focusing on Business Transformation and Human-AI Collaboration

Thinking Machines, a leading data science firm, has partnered with OpenAI to help businesses in the Asia Pacific region leverage artificial intelligence (AI) for tangible outcomes. This partnership makes Thinking Machines the first official Services Partner for OpenAI in the region.

The collaboration comes at a time when AI adoption in the APAC region is on the rise, according to an IBM study which revealed that 61% of enterprises already use AI, yet many struggle to transition beyond proof-of-concept projects and achieve real business impact. Thinking Machines and OpenAI aim to bridge this gap by offering 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 as a capacity-building initiative: “We’re not just introducing new technology; we’re helping organizations build the skills, strategies, and support systems they need to capitalize on AI. Our goal is to reinvent the future of work through human-AI collaboration, making AI truly beneficial for people across the Asia Pacific region.”

Sy identified framing AI adoption as a strategic priority instead of a technical project as one of the biggest hurdles for enterprises. She explained that this misperception often leads to pilot projects that fail to scale.

“Many organizations view AI through a technological lens rather than a transformative one,” Sy said. “This approach results in pilots that never scale because three essential elements are missing: a clear vision of the value to create, redesign of workflows to integrate AI into everyday operations, and investment in employees’ skills to ensure successful adoption.”

Executives, according to Sy, should view AI as a strategic growth driver rather than just a managed risk. She suggested that boards and C-suites play a crucial role in setting this tone by identifying priority outcomes, defining risk appetite, and assigning clear ownership.

“Boards and C-suites establish the foundation: is AI a strategic growth driver or a managed risk? Their role is to name a few key objectives, define risk tolerance, and delegate accountability,” Sy said. Thinking Machines often begins with executive sessions where leaders can explore where tools like ChatGPT add value, how to govern them, and when to scale. “That top-down clarity is what transforms AI from an experiment into an enterprise capability.”

Sy frequently discusses the concept of “reinventing the future of work through human-AI collaboration.” She elaborated on what this looks like in practice: a “human-in-command” approach where people focus on decision-making and exceptions, while AI handles routine tasks such as data retrieval, drafting, or summarizing.

“Human-in-command means redesigning work so that people focus on judgment and exceptions, while AI takes care of data retrieval, drafting, and routine steps with transparency through audit trails and source links,” she said. The results are measured in time saved and quality improvements.

In workshops run by Thinking Machines, professionals using ChatGPT often free up one to two hours per day. Research supports these outcomes—Sy pointed to an MIT study showing a 14% productivity boost for contact centre agents, with the most significant gains observed among less-experienced staff. “This clearly demonstrates that AI can enhance human talent rather than replace it,” she added.

Another focus area for Thinking Machines is agentic AI, which goes beyond single queries to handle multi-step processes. Instead of just answering a question, agentic systems can conduct research, fill forms, and make API calls, managing entire workflows with a human still in control.

“Agentic systems can take work from ‘ask-and-answer’ to multi-step execution: coordinating research, browsing, form-filling, and API calls so teams can deliver faster with a human in command,” Sy said. The promise is faster execution and productivity, but the risks are real. “The principles of human-in-command and auditability remain critical; to avoid the lack of proper guardrails.”

Thinking Machines’ approach involves pairing enterprise controls and auditability with agent capabilities to ensure actions are traceable, reversible, and policy-aligned before scaling. “Our aim is to create a reliable pattern that respects local context while maintaining scalability,” Sy explained.

While adoption is accelerating, governance often lags behind. Sy advised against treating governance as paperwork instead of an integral part of daily work.

“We keep humans in command and make governance visible in daily work: use approved data sources, enforce role-based access, maintain audit trails, and require human decision points for sensitive actions,” she said. Thinking Machines also emphasizes what it calls “control + reliability”: restricting retrieval to trusted content and returning answers with citations. Workflows are then adapted to local rules in sectors such as finance, government, and healthcare.

For Sy, success isn’t measured by the volume of policies but by auditability and exception rates. “Good governance accelerates adoption because teams trust what they ship,” she said.

The cultural and linguistic diversity of the Asia Pacific region poses unique challenges for scaling AI. A one-size-fits-all model doesn’t work. Sy emphasized that the right strategy is to build locally first and then scale deliberately.

“Global templates fail when they disregard local teams’ work culture. The strategy should be build locally, scale deliberately: fit the AI to local languages, forms, policies, and escalation paths; then standardize the parts that travel such as your governance pattern, data connectors, and impact metrics,” she said.

That’s the approach Thinking Machines has taken in Singapore, the Philippines, and Thailand—prove value with local teams first, then roll out region by region. The goal is not a uniform chatbot but a reliable pattern that respects local context while maintaining scalability.

When asked about the skills that will matter most in an AI-enabled workplace, Sy emphasized that scale comes from skills, not just tools. She broke this down into three categories:

“When leaders and teams share that foundation, adoption moves from experimenting to repeatable, production-level results,” she said. In Thinking Machines’ programs, many professionals report saving one to two hours per day after just a one-day workshop. To date, more than 10,000 people across roles have been trained, and Sy noted the pattern is consistent: “skills + governance unlock scale.”

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

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

A practical example is a system Thinking Machines built with the Bank of the Philippine Islands. Called BEAi, it’s a retrieval-augmented generation (RAG) system that supports English, Filipino, and Taglish. It returns answers linked to sources with page numbers and understands policy supersession, turning complex policy documents into everyday guidance for staff. “That’s what ‘AI-native’ looks like in practice,” Sy said.

The partnership with OpenAI will initially focus on programs in Singapore, the Philippines, and Thailand through Thinking Machines’ regional offices before expanding further across APAC. Future plans include tailoring services to sectors such as finance, retail, and manufacturing, where AI can address specific challenges and unlock new opportunities.

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 allow organizations to transition from pilots to impact. When leaders, teams, and technology align, that’s when AI delivers lasting value.”