Thinking Machines Data Science is joining forces with OpenAI to help more businesses across Asia Pacific turn artificial intelligence into measurable results. The collaboration makes Thinking Machines the first official Services Partner for OpenAI in the region.

The partnership comes as AI adoption in APAC continues to rise. An IBM study found that 61% of enterprises already use AI, yet many struggle to move beyond pilot projects and deliver real business impact. Thinking Machines and OpenAI aim to change that by offering executive training on ChatGPT Enterprise, support for building custom AI applications, and guidance on embedding AI into everyday operations.

Stephanie Sy, Founder and CEO of Thinking Machines, framed the partnership around capability building: “We’re not just bringing in new technology but we’re helping organisations build the skills, strategies, and support systems they need to take advantage of AI. For us, it’s about reinventing the future of work through human-AI collaboration and making AI truly work for people across the Asia Pacific region.”

Turning AI pilots into results with Thinking Machines

In an interview with AI News, Sy explained that one of the biggest hurdles for enterprises is how they frame AI adoption. Too often, organisations see it as a technology acquisition rather than a business transformation. That approach leads to pilots that stall or fail to scale.

“The main challenge is that many organisations approach AI as a technology acquisition rather than a business transformation,” she said. “This leads to pilots that never scale because three fundamentals are missing: clear leadership alignment on the value to create, redesign of workflows to embed AI into how work gets done, and investment in workforce skills to ensure adoption. Get those three right—vision, process, people—and pilots scale into impact.”

Leadership at the centre

Many executives still treat AI as a technical project rather than a strategic priority. Sy believes that boards and C-suites need to set the tone. Their role is to decide whether AI is a growth driver or just a managed risk.

“Boards and C-suites set the tone: Is AI a strategic growth driver or a managed risk? Their role is to name a few priority outcomes, define risk appetite, and assign clear ownership,” she 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 turns AI from an experiment into an enterprise capability.”

Human-AI collaboration in practice

Sy often talks about “reinventing the future of work through human-AI collaboration.” She explained what this looks like in practice: a “human-in-command” approach where people focus on judgment, decision-making, and exceptions, while AI handles routine steps like retrieval, drafting, or summarising.

“Human-in-command means redesigning work so people focus on judgment and exceptions, while AI takes on 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 biggest gains seen among less-experienced staff. “That’s clear evidence AI can elevate human talent rather than displace it,” she added.

Agentic AI with Thinking Machines’ guardrails

Another area of focus 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 manage research, fill forms, and make API calls, coordinating entire workflows with a human still in charge.

“Agentic systems can take work from ‘ask-and-answer’ to multi-step execution: coordinating research, browsing, form-filling, and API calls so teams ship 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. Our approach is to pair enterprise controls and auditability with agent capabilities to ensure actions are traceable, reversible, and policy-aligned before we scale.”

Governance that builds trust

While adoption is accelerating, governance often lags behind. Sy cautioned that governance fails when it’s treated as paperwork instead of 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 explained. Thinking Machines also applies 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 in the volume of policies but in auditability and exception rates. “Good governance accelerates adoption because teams trust what they ship,” she said.

Local context, regional scale

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

“Global templates fail when they ignore how local teams work. The playbook is build locally, scale deliberately: fit the AI to local language, forms, policies, and escalation paths; then standardise 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 aim is not a uniform chatbot but a reliable pattern that respects local context while maintaining scalability.

Skills over tools

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

• Executive literacy: the ability for leaders to set outcomes and guardrails, and know when and where to scale AI.
• Workflow design: the redesign of human-AI handoffs, clarifying who drafts, who approves, and how exceptions escalate.
• Hands-on skills: prompting, evaluation, and retrieval from trusted sources so answers are verifiable, not just plausible.

“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.”

Industry transformation ahead

Looking to the next five years, Sy sees AI shifting from drafting to full execution in critical business functions. She expects major 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 CX 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.

Thinking Machines expands AI across APAC

The partnership with OpenAI will start with 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 open 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 let organisations move from pilots to impact. When leaders, teams, and technology come together, that’s when AI delivers lasting value.”

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

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