Thinking Machine Data Science is working with Openai to help more companies in Asia-Pacific turn artificial intelligence into measurable results. With this collaboration, Thinking Machine will become OpenAI’s first official service partner in the region.
The partnership is due to the ever-increasing adoption of AI in APAC. An IBM survey found that 61% of companies already use AI, but many people struggle to make a real business impact beyond pilot projects. Thinking Machine and Openai aim to change that by providing executive training on ChatGPT Enterprises, support for building custom AI applications, and guidance for incorporating AI into everyday operations.
Stephanie SY, founder and CEO of Thinking Machines, has framed a partnership on Capability Building. “We are not only introducing new technologies, but we are also building the skills, strategies and support systems that organizations need to use AI. We are reinventing the future of work through human collaboration and making AI truly work for people with AI.”
Turn AI pilots into thought machine results
In an interview with AI News, SY explained that one of the biggest hurdles for businesses is how to frame AI adoption. In many cases, organizations view it as technology acquisition rather than business transformation. That approach leads to pilots who stall or fail to scale.
“The main challenge is that many organizations approach AI as a skill acquisition rather than a business change,” she said. “This leads to pilots who never scale because they lack three basics. Redesigning workflows, redesigning to embed AI in work outcomes, and investing in workforce skills to ensure recruitment. These three correct, process, get people and expand pilots.”
Leadership at the Center
Many executives still treat AI as a technology project rather than a strategic prioritization. SY believes that the board and C-sweets need to set the tone. Their role is to determine whether AI is a growth driver or merely a management risk.
“The board and C suite set the tone. Is AI a strategic growth driver or a controlled risk? Their role is to give out the outcomes of several priorities, define risk appetites, and assign clear ownership,” she said. Thinking machines often start with executive sessions where leaders can explore where tools like ChatGPT add value, how to manage them, and when to scale. “That top-down clarity transforms AI from experimentation to corporate capabilities.”
In fact, cooperation with humans
SY often talks about “reforming the future of work through human cooperation.” She explained how this actually looks like: a “human” approach in which people focus on judgment, decision-making and exceptions.
“Human command means redesigning work, so people focus on judgments and exceptions, AI takes on searching, drafting, and routine steps, making audit trails and source links transparent,” she said. Results are measured in time savings and quality improvements.
In workshops run by Thinking Machines, experts using ChatGpt are often freed for 1-2 hours a day. The research supports these findings. This points to MIT research showing that 14% of contact center agents are increasing productivity, and has the greatest benefit among less experienced staff. “It’s clear evidence that AI can enhance human talent rather than replace it,” she added.
Agent AI with thought machine guardrail
Another focus area of thought machine is Agent AI. This handles multi-step processes beyond a single query. In addition to answering questions, the agent system can manage research, fill out forms, make API calls, and still coordinate the entire workflow with the person in charge.
“Agent systems can take on tasks ranging from “and-and-wor” to multi-step execution. With research, browsing, foam filling, and coordination of API calls, teams will ship faster with human commanders,” Sy said. Promise is fast execution and productivity, but risk is real. “The principles of human command and auditability remain important to avoid the lack of proper guardrails. Our approach is to pair enterprise control and auditability with agent functionality to ensure trackable, reversible, and policy before expansion.”
Governance that builds trust
Adoptions are accelerating, but governance is often behind. Sy warned that governance will fail if it is treated as a document rather than a part of daily work.
“We command humans and make governance visible in our daily work. We use approved data sources, implement role-based access, maintain audit trails, and request human decision points for sensitive behavior,” she explained. Thinking machines apply what is called “control + reliability.” Limit searches to trustworthy content and respond with quotes. The workflow then complies with local rules for sectors such as finance, government, and healthcare.
In the case of SY, success is measured by auditability and exception rate, not by policy amount. “Good governance accelerates adoption because teams trust what they ship,” she said.
Local context, regional scale
Cultural and linguistic diversity in the Asia-Pacific region poses a unique challenge for scaling AI. The all-purpose model won’t work. SY emphasized that a proper playbook is to build locally first and then intentionally expand.
“Global templates fail if you ignore the mechanisms of local teams. Playbooks are built locally and intentionally expand. They adapt AI to local languages, forms, policies, escalation paths, and standardize moving parts such as governance patterns, data connectors, impact metrics,” she said.
This is the approach adopted by thinking machines in Singapore, the Philippines and Thailand. First promote value with local teams, then roll out by region. The goal is not a uniform chatbot, but a reliable pattern that respects local context while maintaining scalability.
Skills for tools
When asked about the most important skills in an AI-enabled workplace, Sy noted that scale comes from skills as well as tools. She divided this into three categories.
Executive Literacy: Ability for leaders to set out results and guardrails and know when and where to scale AI.workflow design: Human handoff redesign, who approve drafts, who approve, how do exceptions escalate?
“When leaders and teams share that foundation, adoption moves from experiments to repeatable production-level outcomes,” she said. In the Thinking Machine program, many experts report savings of 1-2 hours a day after a day’s workshop. To date, more than 10,000 people have been trained across roles, and Sy noted that the pattern is consistent. “Skill + Governance Rock Scale.”
Future industry transformation
Aiming for the next five years, SY is watching AI move from drafting to full execution in full business functions. She expects significant benefits in software development, marketing, service operations and supply chain management.
“The next wave shows three specific patterns: Financial Policy Awareness Assistant, Manufacturing Supply Chain Capolit, and Retail’s personalized but compliant CX is built with human checkpoints and verifiable sources, allowing leaders to scale with confidence,” she said.
A practical example is a systems thinking machine built with banks in the Philippine Islands. Called Beai, it is a high-generation (RAG) system of search that supports English, Filipinos, and Taglish. Returns answers linked to sources with page numbers, understand policy supersessions, and turn complex policy documents into everyday guidance for staff. “That’s what actually looks like ‘ai-native’,” Sy said.
Thinking Machine Extends AI to APAC
The partnership with OpenAI will begin with programs in Singapore, the Philippines and Thailand before expanding further across APAC. Future plans include adjusting services to sectors such as finance, retail and manufacturing, where AI can address specific challenges and open up new opportunities.
For SY, the goal is clear. “AI adoption is not just about experimenting with new tools. It’s about building visions, processes and skills that allow organizations to move from pilot to impact. When leaders, teams and technology come together, AI offers lasting value.”
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