Thinking Machines Data Science has become the first official OpenAI Services Partner in the Asia-Pacific region. The collaboration is designed to help enterprises move beyond AI pilot projects and deliver measurable business outcomes. With AI adoption rising rapidly—an IBM study found that 61% of APAC enterprises already use AI—the challenge is no longer experimentation but scaling real impact.
Through this partnership, organisations will gain access to executive training on ChatGPT Enterprise, support for custom AI applications, and guidance on embedding AI into daily operations.
From Pilots to Impact
Stephanie Sy, Founder and CEO of Thinking Machines, emphasises that many companies treat AI adoption as a technology purchase rather than a business transformation. This mindset often leads to stalled pilots. According to Sy, three fundamentals are required for scale: leadership alignment, redesigned workflows, and workforce skills. When these are in place, AI moves from isolated experiments to enterprise-level capability.
The Role of Leadership
Sy stresses that boards and executives must lead AI strategy, framing it either as a growth driver or a managed risk. Thinking Machines often begins with executive workshops where leaders define outcomes, set guardrails, and assign ownership. This top-down clarity enables organisations to align resources and scale adoption effectively.
Human-in-Command Collaboration
Rather than replacing workers, the company promotes a “human-in-command” approach. AI handles routine tasks like drafting, retrieval, and summarisation, while humans focus on judgment and decision-making. This redesign of workflows has already shown tangible benefits: professionals in Thinking Machines’ programs often report saving one to two hours per day, supported by research such as MIT’s finding of a 14% productivity boost in contact centres using AI tools.
Building Guardrails for Agentic AI
Thinking Machines is also exploring agentic AI—systems capable of handling multi-step processes, from research to form-filling. While the potential productivity gains are significant, the company insists on embedding enterprise controls, auditability, and human oversight to ensure trust and safety in deployment.
Governance as an Enabler
Sy notes that governance should not be paperwork but part of everyday workflows. Thinking Machines promotes “control + reliability” by using approved data sources, role-based access, audit trails, and human checkpoints for sensitive actions. By building trust through transparency and verifiable outputs, governance becomes a driver of adoption rather than a barrier.
Scaling With Local Context
The Asia-Pacific region’s cultural and linguistic diversity makes AI adoption particularly complex. Thinking Machines advocates a “build locally, scale deliberately” approach—tailoring solutions to local languages, policies, and workflows before rolling them out regionally. This model has already been applied in markets such as Singapore, the Philippines, and Thailand.
Skills as the Real Differentiator
Sy believes scaling AI is less about tools and more about skills. She highlights three critical areas: executive literacy for leadership, workflow design for human-AI handoffs, and hands-on abilities like prompting and evaluation. To date, more than 10,000 professionals have been trained through Thinking Machines’ programs, with consistent time-saving and productivity results.
What’s Next for AI in APAC
Looking ahead, Sy expects AI to move from drafting support to full execution in areas like finance, supply chain management, and customer experience. Already, Thinking Machines has built systems like BEAi for the Bank of the Philippine Islands, a multilingual retrieval-augmented generation tool that ensures accuracy, compliance, and accessibility.
The partnership with OpenAI is set to expand across APAC, beginning with Singapore, the Philippines, and Thailand. For Sy, the ultimate goal is clear: AI adoption must be anchored in vision, processes, and skills to deliver long-term value.
